e-ISBN :- 978-81-963532-3-0 Insights into new age AIdriven teaching & learning pedagogy-A critical analysis by Dr. Raja Roy Choudhury Mr. Budha Chandra Singha Published by International Society for Green, Sustainable Engineering & Management ISO 9001:2015 certified Certified as Research Organization, Under Section 199 Rule 435/435A of Kolkata Municipal Corporation Approved organization under NITI Aayong , Govt of India Approved organization under Ministry of Corporate Affairs, Govt of India 2024

Insights into new age AI-driven teaching & learning pedagogy-A critical analysis Insights into new age AI-driven teaching & learning pedagogy-A critical analysis By Dr. Raja Roy Choudhury Mr. Budha Chandra Singha Publisher’s Name International Society for Green, Sustainable Engineering and Management ISO 9001:2015 certified for Research, Development &Training (Certified as Research Organization, Under Section 199 Rule 435/435A Act 1980 Kolkata Municipal Corporation) Approved organization under NITI Ayog, Govt of India Approved organization under Ministry of Corporate Affairs, Govt of India Member The Indian Science Congress Association Indian Statistical Institute Indian Meteorological Society Certificate of Enlistment as Research Organization, KMC 2024

© International Society for Green, Sustainable Engineering and Management Editor in Chief: Dr.Debaprayag Chaudhuri, Chairman Production Editor: Mrs.Soma Das Chaudhuri Published in India by International Society for Green, Sustainable Engineering and Management 94,Garfa Main Road, Ground Floor, Jadavpur, Kolkata-700 075,West Bengal India Mobile:0091 96 74 76 61 80 Email: isgsem.research.kolkata@gmail.com Website: http://isgsemkolkata.blogspot.com Copyright © 2024.All rights reserved. No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, mechanical or photocopying, recording, or otherwise without the prior permission of the publisher. The society names used in this set are for identification purposes only. 1st Edition: November ’2024 e-ISBN: - 978-81-963532-3-0 650 Insights into new age AI-driven teaching & learning pedagogy-A critical analysis e-ISBN: - 978-81-963532-3-0 Details of the Authors Dr. Raja Roy Choudhury: Dr. Raja Roy Choudhury, Professor of Practice at Dr. D. Y. Patil B-School, India, is highly qualified in the world of behavioral health and leadership sciences. He holds Ph.D. degrees in Economics and Psychology. He has 36 years of experience in the areas of retail, education, behavioral health and management consulting. Address: Dr. D. Y. Patil B-School Sr. No. 87-88, Bengaluru-Mumbai Express Bypass, Tathawade, Pune, Maharashtra 411033 E-mail: drrajaroy.choudhury@dpu.edu.in Mr. Budha Chandra Singha: Mr. Budha Chandra Singha, Assistant Professor at Dr. D Y Patil B-School, Quantitative Techniques and Analytics. He has taught at several BIndia, is a Post Graduate in Business Management and has 11 years of experience in teaching schools in India. Address: Dr. D. Y. Patil B-School Sr. No. 87-88, Bengaluru-Mumbai Express Bypass, Tathawade, Pune, Maharashtra 411033 E-mail: budha.chandra@dpu.edu.in

e-ISBN :- 978-81-963532-3-0 Insights into new age AI-driven teaching & learning pedagogy- A critical analysis By Dr Raja Roy Choudhury Professor of Practice Dr D Y Patil Business School, Pune Mr. Budha Chandra Singha Assistant Professor Dr D Y Patil Business School, Pune e-ISBN :- 978-81-963532-3-0

Table of Contents 1. Introduction ...........………………........……………………………...1 1.1. Rising Interest in AI in Education ..……………………………2 1.2. Three Reasons to Address AI in Education Now ...……………4 1.3. Toward Policies for AI in Education .....……...…………..……6 2. Building Ethical, Equitable Policies Together ……………………...10 2.1. Guiding Questions ...…...……………………………….….....10 2.2. Foundation 1: Center People (Parents, Educators, and Students) ...................................................................................................11 2.3. Foundation 2: Advance Equity ...……………………….….....12 2.4. Foundation 3: Ensure Safety, Ethics, and Effectiveness .….…14 2.5. Foundation 4: Promote Transparency ........…………………...15 2.6. Overview of Document ...……………………………………..17 3. What is AI? ..………………..……………………..………………...19 3.1. Perspective: Human-Like Reasoning ....…...………………….20 3.2. Perspective: An Algorithm that Pursues a Goal .......................21 3.3. Perspective: Intelligence Augmentation .......…………………23 3.4. Definition of “Model” ..............................................................24 3.5. Insight: AI Systems Enable New Forms of Interaction ............25 3.6. Key Recommendation: Human in the Loop AI ........................27 4. Learning .............................................................................................30 4.1. Insight: AI Enables Adaptivity in Learning .............................30 4.2. Intelligent Tutoring Systems: An Example of AI Models ........32 4.3. Important Directions for Expanding AI-Based Additivity .......33 4.4. A Duality: Learning With and About AI ..................................37 4.5. A Challenge: Systems Thinking About AI in Education ..........38 4.6. Open Questions about AI for Learning ....................................39 4.7. Key Recommendation: Seek AI Models Aligned to a Vision for Learning ....................................................................................40 5. Teaching .............................................................................................42 5.1. Always Center Educators in Instructional Loops .....................42 5.2. Insight: Using AI to Improve Teaching Jobs ............................44 5.3. Preparing and Supporting Teachers in Planning and Reflecting ...................................................................................................49 5.4. Designing, Selecting, and Evaluating AI Tools ........................51 5.5. Challenge: Balancing Human and Computer Decision-Making ...................................................................................................51 5.6. Challenge: Making Teaching Jobs Easier While Avoiding Surveillance ..............................................................................53 5.7. Challenge: Responding to Students’ Strengths While Protecting Their Privacy ............................................................................54 5.8. Questions Worth Asking About AI for Teaching .....................56 5.9. Key Recommendation: Inspectable, Explainable, Over ridable AI ..............................................................................................57 6. Formative Assessment ........................................................................61 6.1. Building on Best Practices ........................................................61 6.2. Implications for Teaching and Learning ..................................63 6.3. Insight: AI Can Enhance Feedback Loops ...............................64 6.4. An Example: Automated Essay Scoring ...................................66 6.5. Key Opportunities for AI in Formative Assessment ................67 6.6. Key Recommendation: Harness Assessment Expertise to Reduce Bias ...........................................................................................69 6.7. Related Questions .....................................................................70 7. Research and Development ................................................................71 7.1. Insight: Research Can Strengthen the Role of Context in AI ...72 7.2. Attention to the Long Tail of Learner Variability ................... 75 7.3. Partnership in Design-Based Research .....................................78 7.4. Re-thinking Teacher Professional Development ......................79 7.5. Connecting with Public Policy .................................................81 7.6. Key Recommendation: Focus R&D on Addressing Context ...82 7.7. Ongoing Questions for Researchers .........................................83 7.8. Desired National R&D Objectives ...........................................84 8. Recommendations ..............................................................................86 8.1. Insight: Aligning AI to Policy Objectives ................................86 8.2. Calling Education Leaders to Action ........................................88

8.3. Recommendation #1: Emphasize Humans in the Loop ............88 8.4. Recommendation #2: Align AI Models to a Shared Vision for Education ..................................................................................91 8.5. Recommendation #3: Design Using Modern Learning Principles ...................................................................................................94 8.6. Recommendation #4: Prioritize Strengthening Trust ...............96 8.7. Recommendation #5: Inform and Involve Educators ...............97 8.8. Recommendation #6: Focus R&D on Addressing Context and Enhancing Trust and Safety ............................……………......99 8.9. Recommendation #7: Develop Education-Specific Guidelines and Guardrails ........................................................................102 8.10. Next Step ................................................................................103 9. Common Acronyms and Abbreviations ...........................................105 10. Acknowledgements ..........................................................................106 11. References ........................................................................................107 Chapter 1: Introduction 1. Introduction The U.S. Department of Education (Department) is committed to supporting the use of technology to improve teaching and learning and to support innovation throughout educational systems. This report addresses the clear need for sharing knowledge and developing policies for “Artificial Intelligence,” a rapidly advancing class of foundational capabilities that are increasingly embedded in all types of educational technology systems and are also available to the public. We will consider “educational technology” (edtech) to include both (a) technologies specifically designed for educational use, as well as (b) general technologies that are widely used in educational settings. Recommendations in this report seek to engage teachers, educational leaders, policymakers, researchers, and educational technology innovators and providers as they work together on pressing policy issues that arise as Artificial Intelligence (AI) is used in education. AI can be defined as “automation based on associations.” When computers automate reasoning based on associations in data (or associations deduced from expert knowledge), two shifts fundamental to AI occur and shift computing beyond conventional edtech: (1) from capturing data to detecting patterns in data and (2) from providing access to instructional resources to automating decisions about instruction and other educational processes. Detecting patterns and automating decisions are leaps in the level of responsibilities that can be delegated to a computer system. The process of developing an AI system may lead to bias in how patterns are detected and unfairness in how decisions are automated. Thus, educational systems must govern their use of AI systems. This report P a g e | 1

Chapter 1: Introduction describes opportunities for using AI to improve education, recognizes challenges that will arise, and develops recommendations to guide further policy development. 1.1. Rising Interest in AI in Education Today, many priorities for improvements to teaching and learning are unmet. Educators seek technology-enhanced approaches addressing these priorities that would be safe, effective, and scalable. Naturally, educators wonder if the rapid advances in technology in everyday lives could help. Like all of us, educators use AI-powered services in their everyday lives, such as voice assistants in their homes; tools that can correct grammar, complete sentences, and write essays; and automated trip planning on their phones. Many educators are actively exploring AI tools as they are newly released to the public. Educators see opportunities to use AI-powered capabilities like speech recognition to increase the support available to students with disabilities, multilingual learners, and others who could benefit from greater adaptivity and personalization in digital tools for learning. They are exploring how AI can enable writing or improving lessons, as well as their process for finding, choosing, and adapting material for use in their lessons. Educators are also aware of new risks. Useful, powerful functionality can also be accompanied with new data privacy and security risks. Educators recognize that AI can automatically produce output that is inappropriate or wrong. They are wary that the associations or automations created by AI may amplify unwanted biases. They have noted new ways in which students may represent others’ work as their own. They are well-aware of “teachable moments” and pedagogical strategies that a human teacher can address but are undetected or misunderstood by AI P a g e | 2 Chapter 1: Introduction models. They worry whether recommendations suggested by an algorithm would be fair. Educators’ concerns are manifold. Everyone in education has a responsibility to harness the good to serve educational priorities while also protecting against the dangers that may arise as a result of AI being integrated in edtech. To develop guidance for edtech, the Department works closely with educational constituents. These constituents include educational leaders—teachers, faculty, support staff, and other educators—researchers; policymakers; advocates and funders; technology developers; community members and organizations; and, above all, learners and their families/caregivers. Recently, through its activities with constituents, the Department noticed a sharp rise in interest and concern about AI. For example, a 2021 field scan found that developers of all kinds of technology systems—for student information, classroom instruction, school logistics, parent- teacher communication, and more—expect to add AI capabilities to their systems. Through a series of four listening sessions conducted in June and August 2022 and attended by more than 700 attendees, it became clear that constituents believe that action is required now in order to get ahead of the expected increase of AI in education technology— and they want to roll up their sleeves and start working together. In late 2022 and early 2023, the public became aware of new generative AI chatbots and began to explore how AI could be used to write essays, create lesson plans, produce images, create personalized assignments for students, and more. From public expression in social media, at conferences, and in news media, the Department learned more about risks and benefits of AIenabled chatbots. And yet this report will not focus on a specific AI tool, service, or announcement, because AI-enabled systems P a g e | 3

Chapter 1: Introduction evolve rapidly. Finally, the Department engaged the educational policy expertise available internally and in its relationships with AI policy experts to shape the findings and recommendations in this report. 1.2. Three Reasons to Address AI in Education Now “I strongly believe in the need for stakeholders to understand the cyclical effects of AI and education. By understanding how different activities accrue, we have the ability to support virtuous cycles. Otherwise, we will likely allow vicious cycles to perpetuate.” —Lydia Liu During the listening sessions, constituents articulated three reasons to address AI now: First, AI may enable achieving educational priorities in better ways, at scale, and with lower costs. Addressing varied unfinished learning of students due to the pandemic is a policy priority, and AI may improve the adaptivity of learning resources to students’ strengths and needs. Improving teaching jobs is a priority, and via automated assistants or other tools, AI may provide teachers greater support. AI may also enable teachers to extend the support they offer to individual students when they run out of time. Developing resources that are responsive to the knowledge and experiences students bring to their learning—their community and cultural assets—is a priority, and AI may enable greater customizability of curricular resources to meet local needs. P a g e | 4 Chapter 1: Introduction As seen in voice assistants, mapping tools, shopping recommendations, essay-writing capabilities, and other familiar applications, AI may enhance educational services. Second, urgency and importance arise through awareness of system-level risks and anxiety about potential future risks. For example, students may become subject to greater surveillance. Some teachers worry that they may be replaced—to the contrary, the Department firmly rejects the idea that AI could replace teachers. Examples of discrimination from algorithmic bias are on the public’s mind, such as a voice recognition system that doesn’t work as well with regional dialects, or an exam monitoring system that may unfairly identify some groups of students for disciplinary action. Some uses of AI may be infrastructural and invisible, which creates concerns about transparency and trust. AI often arrives in new applications with the aura of magic, but educators and procurement policies require that edtech show efficacy. AI may provide information that appears authentic, but actually is inaccurate or lacking a basis in reality. Of the highest importance, AI brings new risks in addition to the well-known data privacy and data security risks, such as the risk of scaling pattern detectors and automations that result in “algorithmic discrimination” (e.g., systematic unfairness in the learning opportunities or resources recommended to some populations of students). Third, urgency arises because of the scale of possible unintended or unexpected consequences. When AI enables instructional decisions to be automated at scale, educators may discover unwanted consequences. In a simple example, if AI adapts by speeding curricular pace for some students and by P a g e | 5

Chapter 1: Introduction slowing the pace for other students (based on incomplete data, poor theories, or biased assumptions about learning), achievement gaps could widen. In some cases, the quality of available data may produce unexpected results. For example, an AI-enabled teacher hiring system might be assumed to be more objective than human-based résumé scoring. Yet, if the AI system relies on poor quality historical data, it might deprioritize candidates who could bring both diversity and talent to a school’s teaching workforce. In summary, it is imperative to address AI in education now to realize key opportunities, prevent and mitigate emergent risks, and tackle unintended consequences. 1.3. Toward Policies for AI in Education The 2023 AI Index Report from the Stanford Institute for Human-Centered AI has documented notable acceleration of investment in AI as well as an increase of research on ethics, including issues of fairness and transparency. Of course, research on topics like ethics is increasing because problems are observed. Ethical problems will occur in education, too. The report found a striking interest in 25 countries in the number of legislative proposals that specifically include AI. In the United States, multiple executive orders are focused on ensuring AI is trustworthy and equitable, and the White House Office of Science and Technology Policy has introduced a Blueprint for an AI Bill of Rights (Blueprint) that provides principles and practices that help achieve this goal. These initiatives, along with other AI-related policy activities occurring in both the executive and legislative branches, will guide the use of AI throughout all sectors of society. In Europe, P a g e | 6 Chapter 1: Introduction the European Commission recently released Ethical guidelines on the use of artificial intelligence (AI) and data in teaching and learning for educators. AI is moving fast and heralding societal changes that require a national policy response. In addition to broad policies for all sectors of society, education-specific policies are needed to address new opportunities and challenges within existing frameworks that take into consideration federal student privacy laws (such as the Family Educational Rights and Privacy Act, or FERPA), as well as similar state related laws. AI also makes recommendations and takes actions automatically in support of student learning, and thus educators will need to consider how such recommendations and actions can comply with laws such as the Individuals with Disabilities Education Act (IDEA). We discuss specific policies in the concluding section. Figure 1: Research about AI is growing rapidly. Other indicators, such as dollars invested and number of people employed, show similar trends. P a g e | 7

Chapter 1: Introduction AI is advancing exponentially (see Figure 1), with powerful new AI features for generating images and text becoming available to the public, and leading to changes in how people create text and images. The advances in AI are not only happening in research labs but also are making news in mainstream media and in educational-specific publications. Researchers have articulated a range of concepts and frameworks for ethical AI, as well as for related concepts such as equitable, responsible, and human-centered AI. Listening session participants called for building on these concepts and frameworks but also recognized the need to do more; participants noted a pressing need for guardrails and guidelines that make educational use of AI advances safe, especially given this accelerating pace of incorporation of AI into mainstream technologies. As policy development takes time, policy makers and educational constituents together need to start now to specify the requirements, disclosures, regulations, and other structures that can shape a positive and safe future for all constituents—especially students and teachers. Policies are urgently needed to implement the following: 1. Leverage automation to advance learning outcomes while protecting human decision making and judgment; 2. Interrogate the underlying data quality in AI models to ensure fair and unbiased pattern recognition and decision making in educational applications, based on accurate information appropriate to the pedagogical situation; P a g e | 8 Chapter 1: Introduction 3. Enable examination of how particular AI technologies, as part of larger edtech or educational systems, may increase or undermine equity for students; and 4. Take steps to safeguard and advance equity, including providing for human checks and balances and limiting any AI systems and tools that undermine equity. P a g e | 9

Chapter 2: Building Ethical, Equitable Policies Together 2. Building Ethical, Equitable Policies Together In this report, we aim to build on the listening sessions the Department hosted to engage and inform all constituents involved in making educational decisions so they can prepare for and make better decisions about the role of AI in teaching and learning. AI is a complex and broad topic, and we are not able to cover everything nor resolve issues that still require more constituent engagement. This report is intended to be a starting point. The opportunities and issues of AI in education are equally important in K-12, higher education, and workforce learning. Due to scope limitations, the examples in this report will focus on K -12 education. The implications are similar at all levels of education, and the Department intends further activities in 2023 to engage constituents beyond K-12 schools. 2.1. Guiding Questions Understanding that AI increases automation and allows machines to do some tasks that only people did in the past leads us to a pair of bold, overarching questions: 1. What is our collective vision of a desirable and achievable educational system that leverages automation to advance learning while protecting and centering human agency? 2. How and on what timeline will we be ready with necessary guidelines and guardrails, as well as convincing evidence of positive impacts, so that P a g e | 10 Chapter 2: Building Ethical, Equitable Policies Together constituents can ethically and equitably implement this vision widely? In the Learning, Teaching, and Assessment sections of this report, we elaborate on elements of an educational vision grounded in what today’s learners, teachers, and educational systems need, and we describe key insights and next steps required. Below, we articulate four key foundations for framing these themes. These foundations arise from what we know about the effective use of educational technology to improve opportunity, equity, and outcomes for students and also relate to the new Blueprint. 2.2. Foundation 1: Center People (Parents, Educators, and Students) Education-focused AI policies at the federal, state, and district levels will be needed to guide and empower local and individual decisions about which technologies to adopt and use in schools and classrooms. Consider what is happening in everyday lives. Many of us use AI-enabled products because they are often better and more convenient. For example, few people want to use paper maps anymore; people find that technology helps us plan the best route to a destination more efficiently and conveniently. And yet, people often do not realize how much privacy they are giving up when they accept AI-enabled systems into their lives. AI will bring privacy and other risks that are hard to address only via individual decision making; additional protections will be needed. P a g e | 11

Chapter 2: Building Ethical, Equitable Policies Together There should be clear limits on the ability to collect, use, transfer, and maintain our personal data, including limits on targeted advertising. These limits should put the burden on platforms to minimize how much information they collect, rather than burdening Americans with reading fine print. As protections are developed, we recommend that policies center people, not machines. To this end, a first recommendation in this document (in the next section) is an emphasis on AI with humans in the loop. Teachers, learners, and others need to retain their agency to decide what patterns mean and to choose courses of action. The idea of humans in the loop builds on the concept of “Human Alternatives, Consideration, and Fallback” in the Blueprint and ethical concepts used more broadly in evaluating AI, such as preserving human dignity. A top policy priority must be establishing human in the loop as a requirement in educational applications, despite contrary pressures to use AI as an alternative to human decision making. Policies should not hinder innovation and improvement, nor should they be burdensome to implement. Society needs an education-focused AI policy that protects civil rights and promotes democratic values in the building, deployment, and governance of automated systems to be used across the many decentralized levels of the American educational system. 2.3. Foundation 2: Advance Equity “AI brings educational technology to an inflection point. We can either increase disparities or shrink them, depending on what we do now.” —Dr. Russell Shilling P a g e | 12 Chapter 2: Building Ethical, Equitable Policies Together A recent Executive Order issued by President Biden sought to strengthen the connection among racial equity, education and AI, stating that “members of underserved communities—many of whom have endured generations of discrimination and disinvestment—still confront significant barriers to realizing the full promise of our great Nation, and the Federal Government has a responsibility to remove these barriers” and that the Federal Government shall both “pursue educational equity so that our Nation’s schools put every student on a path to success” and also “root out bias in the design and use of new technologies, such as artificial intelligence.” A specific vision of equity, such as described in the Department’s recent report, Advancing Digital Equity for All is essential to policy discussion about AI in education. This report defines digital equity as “The condition in which individuals and communities have the information technology capacity that is needed for full participation in the society and economy of the United States.” Issues related to racial equity and unfair bias were at the heart of every listening session we held. In particular, we heard a conversation that was increasingly attuned to issues of data quality and the consequences of using poor or inappropriate data in AI systems for education. Datasets are used to develop AI, and when they are non-representative or contain undesired associations or patterns, resulting AI models may act unfairly in how they detect patterns or automate decisions. Systematic, unwanted unfairness in how a computer detects patterns or automates decisions is called “algorithmic bias.” Algorithmic bias could diminish equity at scale with unintended discrimination. As this document discussed in the Formative Assessment section, this is not a new conversation. For decades, constituents have rightly probed whether assessments are unbiased and fair. Just as with assessments, whether an AI model P a g e | 13

Chapter 2: Building Ethical, Equitable Policies Together exhibits algorithmic bias or is judged to be fair and trustworthy is critical as local school leaders make adoption decisions about using AI to achieve their equity goals. We highlight the concept of “algorithmic discrimination” in the Blueprint. Bias is intrinsic to how AI algorithms are developed using historical data, and it can be difficult to anticipate all impacts of biased data and algorithms during system design. The Department holds that biases in AI algorithms must be addressed when they introduce or sustain unjust discriminatory practices in education. For example, in postsecondary education, algorithms that make enrollment decisions, identify students for early intervention, or flag possible student cheating on exams must be interrogated for evidence of unfair discriminatory bias—and not only when systems are designed, but also later, as systems become widely used. 2.4. Foundation 3: Ensure Safety, Ethics, and Effectiveness A central safety argument in the Department’s policies is the need for data privacy and security in the systems used by teachers, students, and others in educational institutions. The development and deployment of AI requires access to detailed data. This data goes beyond conventional student records (roster and gradebook information) to detailed information about what students do as they learn with technology and what teachers do as they use technology to teach. AI’s dependence on data requires renewed and strengthened attention to data privacy, security, and governance (as also indicated in the Blueprint). As AI models are not generally developed in consideration of educational usage or student privacy, the educational application of these models may not be aligned P a g e | 14 Chapter 2: Building Ethical, Equitable Policies Together with the educational institution’s efforts to comply with federal student privacy laws, such as FERPA, or state privacy laws. Figure 2: The Elementary and Secondary Education Act defines four levels of evidence. Further, educational leaders are committed to basing their decisions about the adoption of educational technology on evidence of effectiveness—a central foundation of the Department’s policy. For example, the requirement to base decisions on evidence also arises in the Elementary and Secondary Education Act (ESEA), as amended, which introduced four tiers of evidence (see Figure 2). Our nation’s research agencies, including the Institute of Education Sciences, are essential to producing the needed evidence. The Blueprint calls for evidence of effectiveness, but the education sector is ahead of that game: we need to insist that AI-enhanced edtech rises to meet ESEA standards as well. 2.5. Foundation 4: Promote Transparency The central role of complex AI models in a technology’s detection of patterns and implementation of automation is an important way in which AI-enabled applications, products, and services will be different from conventional edtech. The Blueprint introduces the need for transparency about AI models in terms of disclosure P a g e | 15

Chapter 2: Building Ethical, Equitable Policies Together (“notice”) and explanation. In education, decision makers will need more than notice—they will need to understand how AI models work in a range of general educational use cases, so they can better anticipate limitations, problems, and risks. AI models in edtech will be approximations of reality and, thus, constituents can always ask these questions: How precise are the AI models? Do they accurately capture what is most important? How well do the recommendations made by an AI model fit educational goals? What are the broader implications of using AI models at scale in educational processes? Building on what was heard from constituents, the sections of this report develop the theme of evaluating the quality of AI systems and tools using multiple dimensions as follows: ● About AI: AI systems and tools must respect data privacy and security. Humans must be in the loop. ● Learning: AI systems and tools must align to our collective vision for high-quality learning, including equity. ● Teaching: AI systems and tools must be inspectable, explainable, and provide human alternatives to AI-based suggestions; educators will need support to exercise professional judgment and override AI models, when necessary. ● Formative Assessment: AI systems and tools must minimize bias, promote fairness, and avoid additional testing time and burden for students and teachers. ● Research and Development: AI systems and tools must account for the context of teaching and learning and must work well in educational practice, given variability in students, teachers, and settings. P a g e | 16 Chapter 2: Building Ethical, Equitable Policies Together ● Recommendations: Use of AI systems and tools must be safe and effective for students. They must include algorithmic discrimination protections, protect data privacy, provide notice and explanation, and provide a recourse to humans when problems arise. The people most affected by the use of AI in education must be part of the development of the AI model, system, or tool, even if this slows the pace of adoption. We return to the idea that these considerations fit together in a comprehensive perspective on the quality of AI models in the Recommendations section. 2.6. Overview of Document We begin in the next section by elaborating a definition of AI, followed by addressing learning, teaching, assessment, and research and development. Organizing key insights by these topics keeps us focused on exploring implications for improving educational opportunity and outcomes for students throughout the report. Within these topics, three important themes are explored: 1. Opportunities and Risks. Policies should focus on the most valuable educational advances while mitigating risks. 2. Trust and Trustworthiness. Trust and safeguarding are particularly important in education because we have an obligation to keep students out of harm’s way and safeguard their learning experiences. P a g e | 17

Chapter 2: Building Ethical, Equitable Policies Together 3. Quality of AI Models. The process of developing and then applying a model is at the heart of any AI system. Policies need to support evaluation of the qualities of AI models and their alignment to goals for teaching and learning during the processes of educational adoption and use. “AI in education can only grow at the speed of trust.” —Dr. Dale Allen P a g e | 18 Chapter 3: What is AI? 3. What is AI? Our preliminary definition of AI as automation based on associations requires elaboration. Below we address three additional perspectives on what constitutes AI. Educators will find these different perspectives arise in the marketing of AI functionality and are important to understand when evaluating edtech systems that incorporate AI. One useful glossary of AI for Education terms is the CIRCLS Glossary of Artificial Intelligence Terms for Educators. AI is not one thing but an umbrella term for a growing set of modeling capabilities, as visualized in Figure 3. Figure 3: Components, types, and subfields of AI based on Regona et al (2022). P a g e | 19

Chapter 3: What is AI? 3.1. Perspective: Human-Like Reasoning “The theory and development of computer systems able to perform tasks normally requiring human intelligence such as, visual perception, speech recognition, learning, decision-making, and natural language processing.” Broad cultural awareness of AI may be traced to the landmark 1968 film “2001: A Space Odyssey”—in which the “Heuristicallyprogrammed Algorithmic” computer, or “HAL,” converses with astronaut Frank. HAL helps Frank pilot the journey through space, a job that Frank could not do on his own. However, Frank eventually goes outside the spacecraft, HAL takes over control, and this does not end well for Frank. HAL exhibits human-like behaviors, such as reasoning, talking, and acting. Like all applications of AI, HAL can help humans but also introduces unanticipated risks—especially since AI reasons in different ways and with different limitations than people do. The idea of “human-like” is helpful because it can be a shorthand for the idea that computers now have capabilities that are very different from the capabilities of early edtech applications. Educational applications will be able to converse with students and teachers, co-pilot how activities unfold in classrooms, and take actions that impact students and teachers more broadly. There will be both opportunities to do things much better than we do today and risks that must be anticipated and addressed. The “human-like” shorthand is not always useful, however, because AI processes information differently from how people process information. When we gloss over the differences between P a g e | 20 Chapter 3: What is AI? people and computers, we may frame policies for AI in education that miss the mark. 3.2. Perspective: An Algorithm that Pursues a Goal “Any computational method that is made to act independently towards a goal based on inferences from theory or patterns in data.” This second definition emphasizes that AI systems and tools identify patterns and choose actions to achieve a given goal. These pattern recognition capabilities and automated recommendations will be used in ways that impact the educational process, including student learning and teacher instructional decision making. For example, today’s personalized learning systems may recognize signs that a student is struggling and may recommend an alternative instructional sequence. The scope of pattern recognition and automated recommendations will expand. Correspondingly, humans must determine the types and degree of responsibility we will grant to technology within educational processes, which is not a new dilemma. For decades, the lines between the role of teachers and computers have been discussed in education, for example, in debates using terms such as “’computer-aided instruction,” “blended instruction,” and “personalized learning.” Yet, how are instructional choices made in systems that include both humans and algorithms? Today, AI systems and tools are already enabling the adaptation of instructional sequences to student needs to give students feedback and hints, for example, during mathematics problem solving or foreign language learning. This discussion about the use of AI in classroom pedagogy and student learning P a g e | 21

Chapter 3: What is AI? will be renewed and intensify as AI-enabled systems and tools advance in capability and become more ubiquitous. Let’s start with another simple example. When a teacher says, “Display a map of ancient Greece on the classroom screen,” an AI system may choose among hundreds of maps by noting the lesson objectives, what has worked well in similar classrooms, or which maps have desirable features for student learning. In this case, when an AI system suggests an instructional resource or provides a choice among a few options, the instructor may save time and may focus on more important goals. However, there are also forms of AI-enabled automation that the classroom instructor may reject, for example, enabling an AI system or tool to select the most appropriate and relevant readings for students associated with a historical event. In this case, an educator may choose not to utilize AI-enabled systems or tools given the risk of AI creating false facts (“hallucinating”) or steering students toward inaccurate depictions of historical events found on the internet. Educators will be weighing benefits and risks like these daily. Computers process theory and data differently than humans. AI’s success depends on associations or relationships found in the data provided to an algorithm during the AI model development process. Although some associations may be useful, others may be biased or inappropriate. Finding bad associations in data is a major risk, possibly leading to algorithmic discrimination. Every guardian is familiar with the problem: A person or computer may say, “Our data suggests your student should be placed in this class,” and the guardian may well argue, “No, you are using the wrong data. I know my child better, and they should instead be placed in another class.” This problem is not limited exclusively to AI systems and tools, but the use of AI models can amplify the P a g e | 22 Chapter 3: What is AI? problem when a computer uses data to make a recommendation because it may appear to be more objective and authoritative, even if it is not. Although this perspective can be useful, it can be misleading. A human view of agency, pursuing goals, and reasoning includes our human abilities to make sense of multiple contexts. For example, a teacher may see three students each make the same mathematical error but recognize that one student has an Individualized Education Program to address vision issues, another misunderstands a mathematical concept, and a third just experienced a frustrating interaction on the playground; the same instructional decision is therefore not appropriate. However, AI systems often lack data and judgement to appropriately include context as they detect patterns and automate decisions. Further, case studies show that technology has the potential to quickly derail from safe to unsafe or from effective to ineffective when the context shifts even slightly. For this and other reasons, people must be involved in goal setting, pattern analysis, and Decision-making. 3.3. Perspective: Intelligence Augmentation “Augmented intelligence is a design pattern for a human-centered partnership model of people and artificial intelligence (AI) working together to enhance cognitive performance, including learning, decision making, and new experiences.” Foundation #1 (above) keeps humans in the loop and positions AI systems and tools to support human reasoning. “Intelligence Augmentation” (IA) centers “intelligence” and “decision making” in humans but recognizes that people sometimes are overburdened and benefit from assistive tools. AI may help teachers make better decisions because computers notice patterns that teachers can miss. P a g e | 23

Chapter 3: What is AI? For example, when a teacher and student agree that the student needs reminders, an AI system may provide reminders in whatever form a student likes without adding to the teacher’s workload. Intelligence Automation (IA) uses the same basic capabilities of AI, employing associations in data to notice patterns, and, through automation, takes actions based on those patterns. However, IA squarely focuses on helping people in human activities of teaching and learning, whereas AI tends to focus attention on what computers can do. 3.4. Definition of “Model” The above perspectives open a door to making sense of AI. Yet, to assess AI meaningfully, constituents must consider specific models and how they are developed. In everyday usage, the term “model” has multiple definitions. We clarify our intended meaning, which is a meaning similar to “mathematical model,” below. (Conversely, note that “model” as used in “AI model” is unlike the usage in “model school” or “instructional model” as AI model is not a singular case created by experts to serve as an exemplar.) AI models are like financial models: an approximation of reality that is useful for identifying patterns, making predictions, or analyzing alternative decisions. In a typical middle school math curriculum, students use a mathematical model to analyze which of two cell phone plans is better. Financial planners use this type of model to provide guidance on a retirement portfolio. At its heart, AI is a highly advanced mathematical toolkit for building and using models. Indeed, in well-known chatbots, complex essays are written one word at a time. The underlying AI model predicts which next words would P a g e | 24 Chapter 3: What is AI? likely follow the text written so far; AI chatbots use a very large statistical model to add one likely word at a time, thereby writing surprisingly coherent essays. When we ask about the model at the heart of AI, we begin to get answers about “what aspects of reality does the model approximate well?” and “how appropriate is it to the decision to be made?” One could similarly ask about algorithms—the specific decisionmaking processes that an AI model uses to go from inputs to outputs. One could also ask about the quality of the data used to build the model—for example, how representative is that data? Switching among three terms—models, algorithms, and data—will become confusing. Because the terms are closely related, we’ve chosen to focus on the concept of AI models. We want to bring to the fore the idea that every AI model is incomplete, and it's important to know how well the AI model fits the reality we care about, where the model will break down, and how. Sometimes people avoid talking about the specifics of models to create a mystique. Talking as though AI is unbounded in its potential capabilities and a nearly perfect approximation to reality can convey an excitement about the possibilities of the future. The future, however, can be oversold. Similarly, sometimes people stop calling a model AI when its use becomes commonplace, yet such systems are still AI models with all of the risks discussed here. We need to know exactly when and where AI models fail to align to visions for teaching and learning. 3.5. Insight: AI Systems Enable New Forms of Interaction AI models allow computational processes to make recommendations or plans and also enable them to support forms P a g e | 25

Chapter 3: What is AI? of interaction that are more natural, such as speaking to an assistant. AI- enabled educational systems will be desirable in part due to their ability to support more natural interactions during teaching and learning. In classic edtech platforms, the ways in which teachers and students interact with edtech are limited. Teachers and students may choose items from a menu or in a multiple-choice question. They may type short answers. They may drag objects on the screen or use touch gestures. The computer provides outputs to students and teachers through text, graphics, and multimedia. Although these forms of inputs and outputs are versatile, no one would mistake this style of interaction with the way two people interact with one another; it is specific to humancomputer interaction. With AI, interactions with computers are likely to become more like human-to-human interactions (see Figure 4). A teacher may speak to an AI assistant, and it may speak back. A student may make a drawing, and the computer may highlight a portion of the drawing. A teacher or student may start to write something, and the computer may finish their sentence— as when today’s email programs can complete thoughts faster than we can type them. Additionally, the possibilities for automated actions that can be executed by AI tools are expanding. Current personalization tools may automatically adjust the sequence, pace, hints, or trajectory through learning experiences. Actions in the future might look like an AI system or tool that helps a student with homework or a teaching assistant that reduces a teacher’s workload by recommending lesson plans that fit a teacher’s needs and are similar to lesson plans a teacher previously liked. Further, an AIenabled assistant may appear as an additional “partner” in a small P a g e | 26 Chapter 3: What is AI? group of students who are working together on a collaborative assignment. An AI-enabled tool may also help teachers with complex classroom routines. For example, a tool may help teachers with orchestrating the movement of students from a full class discussion into small groups and making sure each group has the materials needed to start their work. Figure 4. Differences that teachers and students may experience in future technologies. 3.6. Key Recommendation: Human in the Loop AI Many have experienced a moment where technology surprised them with an uncanny ability to recommend what feels like a precisely personalized product, song, or even phrase to complete a sentence in a word processor such as the one being used to draft this document. Throughout this supplement, we talk about specific, focused applications where AI systems may bring value (or risks) into education. At no point do we intend to imply that AI can replace a teacher, a guardian, or an educational leader as the P a g e | 27

Chapter 3: What is AI? custodian of their students’ learning. We talk about the limitations of models in AI and the conversations that educational constituents need to have about what qualities they want AI models to have and how they should be used. “We can use AI to study the diversity, the multiplicity of effective learning approaches and think about the various models to help us get a broader understanding of what effective, meaningful engagement might look like across a variety of different contexts.” —Dr. Marcelo Aaron Bonilla Worsley These limitations lead to our first recommendation: that we pursue a vision of AI where humans are in the loop. That means that people are part of the process of noticing patterns in an educational system and assigning meaning to those patterns. It also means that teachers remain at the helm of major instructional decisions. It means that formative assessments involve teacher input and decision making, too. One loop is the cycle of recognizing patterns in what students do and selecting next steps or resources that could support their learning. Other loops involve teachers planning and reflecting on lessons. Response to Intervention is another well-known type of loop. The idea of humans in the loop is part of our broader discussions happening about AI and society, not just AI in education. Interested readers could look for more on human-centered AI, responsible AI, value-sensitive AI, AI for social good, and other similar terms that ally with humans in the loop, such as “humancentered AI.” Exercising judgement and control in the use of AI systems and tools is an essential part of providing the best opportunity to learn for all students—especially when educational decisions carry consequence. AI does not have the broad qualities of P a g e | 28 Chapter 3: What is AI? contextual judgment that people do. Therefore, people must remain responsible for the health and safety of our children, for all students’ educational success and preparation for their futures, and for creating a more equitable and just society. P a g e | 29

Chapter 4: Learning 4. Learning The Department’s long-standing edtech vision sees students as active learners; students participate in discussions that advance their understanding, use visualizations and simulations to explain concepts as they relate to the real world, and leverage helpful scaffolding and timely feedback as they learn. Constituents want technology to align to and build on these and other research-based understandings of how people learn. Educators can draw upon two books titled How People Learn and How People Learn II by the National Academies of Sciences, Engineering, and Medicine for a broad synthesis of what we know about learning. As we shape AIenhanced edtech around research-based principles, a key goal must be to strengthen and support learning for those who have experienced unfavorable circumstances for learning, such as caused by the COVID-19 pandemic or by broader inequities. And we must keep a firm eye toward the forms of learning that will most benefit learners in their future lives in communities and workplaces. Examples of AI supporting learning principles in this section include the following: AI-based tutoring for students as they solve math problems (based on cognitive learning theories), adapting to learners with special needs (based on the Universal Design for Learning framework and related theories), and AI support for effective student teamwork (based on theories in the field called “Computer Supported Collaborative Learning”). 4.1. Insight: AI Enables Adaptivity in Learning Adaptivity has been recognized as a key way in which technology can improve learning. AI can be a toolset for improving the adaptivity of edtech. AI may improve a technology’s ability to meet students where they are, build on their strengths, and grow their knowledge and skills. Because of P a g e | 30 Chapter 4: Learning AI’s powers of work with natural forms of input and the foundational strengths of AI models (as discussed in the What is AI? section), AI can be an especially strong toolkit for expanding the adaptivity provided to students. And yet, especially with AI, adaptivity is always more specific and limited than what a broad phrase like “meet students where they are” might suggest. Core limits arise from the nature of the model at the heart of any specific AI-enabled system. Models are approximations of reality. When important parts of human learning are left out of the model or less fully developed, the resulting adaptivity will also be limited, and the resulting supports for learning may be brittle or narrow. Consequently, this section on learning focuses on one key concept: Work toward AI models that fit the fullness of visions for learning— and avoid limiting learning to what AI can currently model well. AI models are demonstrating greater skills because of advances in what are called “large language models” or sometimes “foundational models.” These very general models still have limits. For example, generative AI models discussed in the mainstream news can quickly generate convincing essays about a wide variety of topics while other models can draw credible images based on just a few prompts. Despite the excitement about foundational models, experts in our listening sessions warned that AI models are narrower than visions for human learning and that designing learning environments with these limits in mind remains very important. The models are also brittle and can’t perform well when contexts change. In addition, they don’t have the same “common sense” judgment that people have, often responding in ways that are unnatural or incorrect. Given the unexpected ways in which foundational models miss the mark, keeping humans in the loop remains highly important. P a g e | 31

Chapter 4: Learning 4.2. Intelligent Tutoring Systems: An Example of AI Models One long-standing type of AI-enabled technology is an Intelligent Tutoring System (ITS). In an early success, scientists were able to build accurate models of how human experts solve mathematical problems. The resulting model was incorporated into a system that would observe student problem solving as they worked on mathematical problems on a computer. Researchers who studied human tutors found that feedback on specific steps (and not just right or wrong solutions) is a likely key to why tutoring is so effective. For example, when a student diverged from the expert model, the system gave feedback to help the student get back on track. Importantly, this feedback went beyond right or wrong, and instead, the model was able to provide feedback on specific steps of a solution process. A significant advancement of AI, therefore, can be its ability to provide adaptivity at the step-bystep level and its ability to do so at scale with modest cost. As a research and development (R&D) field emerged to advance ITS, the work has gone beyond mathematics problems to additional important issues beyond step-by-step problem solving. In the early work, some limitations can be observed. The kinds of problems that an ITS could support were logical or mathematical, and they were closed tasks, with clear expectations for what a solution and solution process should look like. Also, the “approximation of reality” in early AI models related to cognition and not to other elements of human learning, for example, social or motivational aspects. Over time, these early limitations have been addressed in two ways: by expanding the AI models and by involving humans in the loop, a perspective that is also important now. Today, for example, if an ITS specializes in feedback as a student practices, a human teacher could still be responsible for motivating student P a g e | 32 Chapter 4: Learning engagement and self-regulation along with other aspects of instruction. In other contemporary examples, the computer ITS might focus on problem solving practice, while teachers work with students in small groups. Further, students can be in the loop with AI, as is the case with “open learner models”—a type of AI-enabled system that provides information to support student self-monitoring and reflection. Although R&D along the lines of an ITS should not limit the view of what’s possible, such an example is useful because so much research and evaluation has been done on the ITS approach. Researchers have looked across all the available high-quality studies in a meta-analysis and concluded that ITS approaches are effective. Right now, many school systems are looking at high-intensity human tutoring to help students with unfinished learning. Human tutoring is very expensive, and it is hard to find enough high-quality human tutors. With regard to large-scale needs, if it is possible for an ITS to supplement what human tutors do, it might be possible to extend beyond the amount of tutoring that people can provide to students. 4.3. Important Directions for Expanding AI-Based Adaptivity Adaptivity is sometimes referred to as “personalization.” Although this is a convenient term, many observers have noted how imprecise it is. For some educators, personalization means giving learners “voice and choice,” and for others it means that a learning management system recommends an individual “playlist” of activities to each student. Hidden in that imprecision is the reality that many edtech products that personalize do so in limited ways. Adjusting the difficulty and the order of lesson materials are among the two most common ways that edtech products adapt. And yet, any teacher knows there is more to supporting learning than adjusting the difficulty and sequence of P a g e | 33

Chapter 4: Learning materials. For example, a good teacher can find ways to engage a student by connecting to their own past experiences and can shape explanations until they really connect in an “aha!” moment for that student. When we say, “meet the learner where they are,” human teachers bring a much more complete picture of each learner than most available edtech. The teacher is also not likely to “over personalize” (by performing like an algorithm that only presents material for which the learner has expressed interest), thereby limiting the student’s exposure to new topics. The nature of “teachable moments” that a human teacher can grasp is broader than the teachable moments today’s AI models grasp. In our listening sessions, we heard many ways in which the core models in an AI system must be expanded. We discuss these below. 1. From deficit-based to asset-oriented. Listening session attendees noted that the rhetoric around adaptivity has often been deficit-based; technology tries to pinpoint what a student is lacking and then provides instruction to fill that specific gap. Teachers also orient to students' strengths; they find competencies or “assets” a student has and use those to build up the students’ knowledge. AI models cannot be fully equitable while failing to recognize or build upon each student’s sources of competency. AI models that are more asset-oriented would be an advance. 2. From individual cognition to including social and other aspects of learning. The existing adaptivity rhetoric has also tended to focus on individualized learning and mostly on cognitive elements of learning, with motivational and other elements only brought in to support the cognitive learning goals. Attendees observe that their vision for learning is broader than cognition. Social learning is important, for example, especially for students to learn to P a g e | 34 Chapter 4: Learning reason, explain, and justify. For students who are learning English, customized and adaptive support for improving language skills while learning curricular content is clearly important. Developing self-regulation skills is also important. A modern vision of learning is not individualistic; it recognizes that students learn in groups and communities too. 3. From neurotypical to neurodiverse learners. AI models could help in including neurodiverse learners (students who access, process, and interact with the world in less common ways than “neurotypical” students) who could benefit from different learning paths and from forms of display and input that fit their strengths. Constituents want AI models that can support learning for neurodiverse learners and learners with disabilities. Thus, they want AI models that can work with multiple paths to learning and multiple modalities of interaction. Such models should be tested for efficacy, to guard against the possibility that some students could be assigned a “personalized” but inadequate learning resource. In addition, some systems for neurodiverse students are presently underutilized, so designs that support intended use will also be important. 4. From fixed tasks to active, open, and creative tasks. As mentioned above, AI models are historically better at closed tasks like solving a math problem or logical tasks like playing a game. In terms of life-wide and lifelong opportunities, we value learning how to succeed at openended and creative tasks that require extended engagement from the learner, and these are often not purely mathematical or logical. We want students to learn to P a g e | 35

Chapter 4: Learning invent and create innovative approaches. We want AI models that enable progress on open, creative tasks. 5. From correct answers to additional goals. At the heart of many adaptivity approaches now on the market, the model inside the technology counts students' wrong answers and decides whether to speed up, slow down, or offer a different type of learning support. Yet, right and wrong answers are not the only learning goals. We want students to learn how to self-regulate when they experience difficulties in learning, for example, such as being able to persist in working on a difficult problem or knowing how and when to ask for help. We want learners to become skilled in teamwork and in leading teams. As students grow, we want them to develop more agency and to be able to act on their own to advance toward their own learning goals. Listing every dimension of expansion that we heard in our listening sessions is beyond the scope of this report. Some additional dimensions are presented in the following sections on Teaching, Assessment, and Research. For example, in Research, we discuss all the ways in which AI systems have trouble with context—context that humans readily grasp and consider. Overall, constituents in the listening sessions realized we need an ambitious outlook on learning to respond to the future today’s learners face. Constituents were concerned about ways in which AI might narrow learning. For example, if the incorporation of AI into education slowed attention to students’ skills on creative, open-ended tasks and their ability to lead and collaborate in teams, then school districts may be less able to realize their students’ progress in relation to a Portrait of a Graduate who excels in communication and other skills valued in communities and careers. P a g e | 36 Chapter 4: Learning Constituents reminded us that as we conceptualize what we want AI in edtech to accomplish, we must start and constantly revisit a human-centered vision of learning. 4.4. A Duality: Learning With and About AI As AI is brought into schools, two broad perspectives about AI in education arise: (1) AI in support of student learning; and (2) support for learning about AI and related technologies. So far, we’ve discussed AI systems and tools to support student learning and mastery of subjects like mathematics and writing. Yet, it is also important that students learn about AI, critically examine its presence in education and society, and determine its role and value in their own lives and careers. We discuss risks across each section in this report. Here, it is important for students to become more aware of and savvy to the risks of AI—including risks of bias and surveillance—as they appear in all elements of their lives. In the recent past, schools have supported students’ understanding of cybersecurity, for example. AI will bring new risks, and students need to learn about them. We are encouraged by efforts we’ve seen underway that would give students opportunities to learn about how AI works while also giving them opportunities to discuss relevant topics like privacy and security. Other learning goals are noted in the K-12 Computer Science Framework. We’ve seen that students can begin learning about AI in elementary, middle, and high school. They can use AI to design simulations and products that they find exciting. And we’ve seen that students want to talk about the ethics of products they experience in their everyday lives and have much to say about the kinds of products they’d like to see or not see in school. (And later, in the Research section, we note the desire for co-design processes that involve students in creating the next generation of AI-enabled edtech). Overall, it’s P a g e | 37

Chapter 4: Learning important to balance attention to using AI to support learning and giving students opportunities to learn about AI. 4.5. A Challenge: Systems Thinking About AI in Education As AI expands into the educational system, our listening session attendees reminded us that it will be entering parts or locations of the system that are presently dysfunctional. AI is certainly not a fix for broken systems, and instead, must be used with even more care when the systems’ context is unstable or uncertain. “First and foremost, they are getting deployed in educational contexts that are already fragmented and broken and unequal. Technology doesn't discriminate—we do. So, as we think about the application of these new systems, we have to really think about the contextual application of AI.” —Dr. Nicole Turner As discussed previously, because AI systems and tools do not fully align with goals for learning, we have to design educational settings to situate AI in the right place, where educators and other adults can make effective use of these tools for teaching and learning. Within the ITS example, we saw that AI could make learning by practicing math problems more effective, and a whole curricular approach might include roles for teachers that emphasize mathematical practices like argumentation and modeling. Further, small-group work is likely to remain important: Students might work in small groups to use mathematics to predict or justify as they work on responding to a realistic challenge. At the present, one “right place” for people, and not AI, is understanding how learning can be culturally responsive and culturally sustaining, as AI is not even close to being ready to connect learning to the unique strengths in a student’s community and family. P a g e | 38 Chapter 4: Learning 4.6. Open Questions about AI for Learning With advances occurring in the foundations for AI, opportunities to use AI in support of learning are rapidly expanding. As we explore these opportunities, the open questions below deserve ongoing attention: ● To what extent is AI enabling adaptation to students’ strengths and not just deficits? Is AI enabling improved support for learners with disabilities and English language learners? ● How are youth voices involved in choosing and using AI for learning? ● Is AI leading to narrower student activities (e.g., procedural math problems), or the fuller range of activities highlighted in the National Educational Technology Plan (NETP), which emphasizes features such as personalized learning, project-based learning, learning from visualizations, simulations, and virtual reality, as well as learning across school, community, and familial settings? ● Is AI supporting the whole learner, including social dimensions of learning such as enabling students to be active participants in small group and collaborative learning? For example, does AI contribute to aspects of student collaboration we value like shared attention, mutual engagement, peer help, self-regulation, and building on each other’s contributions? ● When AI is used, are students’ privacy and data protected? Are students and their guardians informed about what happens with their data? P a g e | 39

Chapter 4: Learning ● How strong are the processes or systems for monitoring student use of AI for barriers, bias, or other undesirable consequences of AI use by learners? How are emergent issues addressed? ● Is high-quality research or evaluations about the impacts of using the AI system for student learning available? Do we know not only whether the system works but for whom and under what conditions? 4.7. Key Recommendation: Seek AI Models Aligned to a Vision for Learning We’ve called attention to how advances in AI are important to adaptivity but also to ways in which adaptivity is limited by the model’s inherent quality. We noted that a prior wave of edtech used the term “personalized” in differing ways, and it was often important to clarify what personalization meant for a particular product or service. Thus, our key recommendation is to tease out the strengths and limitations of AI models inside forthcoming edtech products and to focus on AI models that align closely to desired visions of learning. AI is now advancing rapidly, and we should differentiate between products that have simple AI-like features inside and products that have more sophisticated AI models. Looking at what’s happening in research and development, we can see significant effort and push toward overcoming these limitations. We noted that decision makers need to be careful about selecting AI models that might narrow their vision for learning, as general artificial intelligence does not exist. And because AI models will always be narrower than real world experience, we need to proceed with systems thinking in which humans are in the loop, with the strengths and weaknesses of the P a g e | 40 Chapter 4: Learning specific educational system considered. We hold that the full system for learning is broader than its AI component. P a g e | 41

Chapter 5: Teaching 5. Teaching Teachers have long envisioned many things that technology could make possible for teachers, their classrooms, and their students but not the changes wrought by the recent pandemic. Today, nearly all teachers have experienced uses of technologies for instruction that no one anticipated. Some of those experiences were positive, and others were not. All of the experiences provide an important context as we think further about teaching and technology. There is a critical need to focus on addressing the challenges teachers experience. It must become easier for teachers to do the amazing work they always do. We must also remember why people choose the teaching profession and ensure they can do the work that matters. This section discusses examples of AI supporting teachers and teaching including these concepts: AI assistants to reduce routine teaching burdens; AI that provides teachers with recommendations for their students’ needs and extends their work with students; and AI that helps teachers to reflect, plan, and improve their practice. “One opportunity I see with AI is being able to reduce the amount of attention I have to give to administrative things and increase the amount of attention I can give to my students with their learning needs in the classroom. So that's the first one that I'd say that I'm super excited about the possibility of AI to support me as a teacher." —Vidula Plante 5.1. Always Center Educators in Instructional Loops To succeed with AI as an enhancement to learning and teaching, we need to always center educators (ACE). Practically speaking, practicing “ACE in AI” means keeping a humanistic view of P a g e | 42 Chapter 5: Teaching teaching front and center. ACE leads the Department to confidently respond “no” when asked “will AI replace teachers?” ACE is not just about making teachers’ jobs easier but also making it possible to do what most teachers want to do. That includes, for example, understanding their students more deeply and having more time to respond in creative ways to teachable moments. To bring more precision to how and where we should center educators, we return to our advocacy for human in the loop AI and ask, what are the loops in which teachers should be centered? Figure 5 suggests three key loops (inspired by research on adaptivity loops): 1.The loop in which teachers make moment-to-moment decisions as they do the immediate work of teaching. 2. The loop in which teachers prepare for, plan, and reflect on teaching, which includes professional development. 3. The loop in which teachers participate in decisions about the design of AI-enabled technologies, participate in selecting the technologies, and shape the evaluation of technologies—thus setting a context for not only their own classroom but those of fellow teachers as well. Figure 5: Three ways to center educators as we conceptualize human in the loop AI P a g e | 43

Chapter 5: Teaching Please note that in the next section, on Formative Assessment, we also discuss teachers’ important role in feedback loops that support students and enable school improvement. That section also includes a discussion of the concepts of “bias” and “fairness,” which are important to teachers. 5.2. Insight: Using AI to Improve Teaching Jobs The job of teaching is notoriously complex, with teachers making thousands of decisions each day. Teachers participate in classroom processes, in interactions with students beyond classrooms, in work with fellow teachers, and in administrative functions. They also are part of their communities and thus are expected to interact with families and caregivers. P a g e | 44 Chapter 5: Teaching If the teacher is able to efficiently predict and understand the range of other answers given by students in the class, it becomes possible to think creatively about the novel answer and figure how and why the student might have generated it. We think about how much easier some everyday tasks have become. We can request and receive alerts and notifications about events. Selecting music that we want to hear used to be a multistep process (even with digital music), and now we can speak the name of a song we want to hear, and it plays. Likewise, mapping a journey used to require a cumbersome study of maps, but now cell phones let us choose among several transportation options to reach a destination. Why can’t teachers be supported to notice changing student needs and provided with supports to enact a technology-rich lesson plan? Why can’t they more easily plan their students’ learning journeys? When things change in a classroom, as they always do, why don’t the tools of the classroom make it easier for teachers to adapt to student strengths and needs on the fly? Figure 6: Teachers work about 50 hours a week, spending less than half the time in direct interaction with students. 45 | P a g e

Chapter 5: Teaching A report by McKinsey first suggested that AI’s initial benefit could be to improve teaching jobs by reducing low-level burdens in administrative or clerical work (Figure 6). The report also suggests that recovered time from AI-enabled technology should be rededicated toward more effective instruction—particularly, outcomes such as reducing the average 11 hours of weekly preparation down to only six. We highlight these opportunities and two others below. 1. Handling low-level details to ease teaching burdens and increase focus on students. A good teacher must master all levels of details, big and small. When working with a particular student, the teacher may wish to later send that student a helpful learning resource. How will they remember to send it? A voice assistant or other forms of an AI assistant could make it easier to stay organized by categorizing simple voice notes for teachers to follow up on after a classroom session ends. We are beginning to see AI- enabled voice assistants in the market, and they could do many simple tasks so that the teachers can stay focused on students. These tasks can include record-keeping, 46 | P a g e Chapter 5: Teaching starting and stopping activities, controlling displays, speakers, and other technologies in the classroom, and providing reminders. Many workers may eventually use assistants to make their jobs easier, and teachers are the most deserving of efforts to ease their jobs now. 2. Extending beyond the teacher's availability with their students but continuing to deliver on the teacher’s intent. Teachers almost always want to do more with each student than they can, given the limited number of hours before the next school day. A teacher may wish to sit with the student as they practice 10 more math problems, giving them ongoing support and feedback. If the teacher can sit with the student for only three problems, perhaps they could delegate to an AI-enabled learning system to help with the rest. Teachers cannot be at their best if on call at all hours to help with homework, but perhaps they can indicate what types of supports, hints, and feedback they want students to receive while studying after school hours. An AI assistant can ensure that students have that support wherever and whenever they do homework or practice skills on their own. Teachers may wish to provide more extensive personal notes to families/caregivers, and perhaps an AI assistant could help with drafts based on students’ recent classroom work. Then, the teacher could review the AI-generated comments and quickly edit where needed before returning it to the student for another draft. AI tools might also help teachers with language translation so they can work with all parents and caregivers of their students. AI tools might also help teachers with awareness. For example, in the next section, Formative Assessment, we note that teachers can’t always know what’s going on for each student and in each 47 | P a g e

Chapter 5: Teaching small group of students; emerging products might signal to the teacher when a student or teacher may need some more personal attention. 3. Making teacher professional development more productive and fruitful. Emerging products already enable a teacher to record her classroom and allow an AI algorithm to suggest highlights of the classroom discussion worth reviewing with a professional development coach. AI can compute metrics, such as whether students have been talking more or less, which are difficult for a teacher to calculate during a lesson. For teachers who want to increase student engagement, these metrics can be a valuable tool. Classroom simulation tools are also emerging and can enable teachers to practice their skills in realistic situations. Simulators can include examples of teaching from a real classroom while changing the faces and voices of the participants so that teaching situations can be shared and discussed among teachers without revealing identities. Note the emphasis above on what listening-session panelist Sarah Hampton said about the human touch. Teachers will feel that AI is helping them teach with a focus on their human connection to their students when the necessary (but less meaningful) burdens of teaching are lessened. In Figure 7, below, see concerns that teachers raised about AI during listening sessions. Figure 7: Concerns raised during the listening session about teaching with AI 48 | P a g e Chapter 5: Teaching 5.3. Preparing and Supporting Teachers in Planning and Reflecting ACE also means preparing teachers to take advantage of possibilities like those listed above and more. In the Research section, we highlight how pre-service education still tends to compartmentalize and inadequately address the topic of technology. That section suggests a need to invest in research about how to deeply integrate technology in pre-service teacher training programs. In-service teachers, too, will need professional development to take advantage of opportunities that AI can provide, like those presented in the Teaching section. Professional development will need to be balanced not only to discuss opportunities but also to inform teachers of new risks, while providing them with tools to avoid the pitfalls of AI. “Humans are well suited to discern the outcomes…because we are the ones that have the capacity for moral reflection and empathy. So, in other words, 49 | P a g e

Chapter 5: Teaching I want the AI to help me really quickly and easily see what my student needs in their learning journey.” —Sarah Hampton By nature, teaching requires significant time in planning as well to account for the breadth of needs across their rosters— especially for inclusive learning environments and students with IEPs and 504 plans. AI could help teachers with recommendations that are tuned to their situation and their ways of practicing teaching and support with adapting found materials to fit their exact classroom needs. For students with an IEP, AI could help with finding components to add to lesson plans to fully address standards and expectations and to meet each student’s unique requirements. Even beyond finding components, AI might help adapt standardized resources to better fit specific needs—for example, providing a voice assistant that allows a student with a visual difficulty to hear material and respond to it or permitting a group of students to present their project using American Sign Language (ASL) which could be audibly voiced for other students using an AI ASL-to-Spoken-English translation capability. Indeed, coordinating IEPs is time-consuming work that might benefit from supportive automation and customized interactivity that can be provided by AI. Reflection is important too. In the bustle of a classroom, it is sometimes difficult to fully understand what a student is expressing or what situations lead to certain positive or negative behaviors. Again, context is paramount. In the moment, teachers may not be aware of external events that could shape their understanding of how students are showing up in their classrooms. Tools that notice patterns and suggest ways to share information might help students and teachers communicate more fully about strengths and needs. 50 | P a g e Chapter 5: Teaching 5.4. Designing, Selecting, and Evaluating AI Tools The broadest loop teachers should be part of is the loop that determines what classroom tools do and which tools are available. Today, teachers already play a role in designing and selecting technologies. Teachers can weigh in on usability and feasibility. Teachers examine evidence of efficacy and share their findings with other school leaders. Teachers already share insights on what is needed to implement technology well. While these concerns will continue, AI will raise new concerns too. For example, the following Formative Assessment section raises concerns about bias and fairness that can lead to algorithmic discrimination. Those concerns go beyond data privacy and security; they raise attention to how technologies may unfairly direct or limit some students’ opportunities to learn. A key takeaway here is that teachers will need time and support so they can stay abreast of both the well-known and the newer issues that are arising and so they can fully participate in design, selection, and evaluation processes that mitigate risks. 5.5. Challenge: Balancing Human and Computer DecisionMaking One major new challenge with AI-enabled tools for teachers is that AI can enable autonomous activity by a computer, and thus when a teacher delegates work to an AI-enabled tool, it may carry on with that work somewhat independently. Professor Inge Molenaar has wondered about the challenges of control in a hybrid teaching scenario: When should a teacher be in control? What can be delegated to a computational system? How can a teacher monitor the AI system and override its decisions or take back control as necessary? 51 | P a g e

Chapter 5: Teaching Figure 8: The tension between human and AI decision making: Who is in control? Figure 8 expresses the tension around control. To the left, the teacher is fully in control, and there is no use of AI in the classroom. To the right, the technology is fully in control with no teacher involved—a scenario which is rarely desirable. The middle ground is not one dimensional and involves many choices. Molenaar analyzed products and suggests some possibilities: ●The technology only offers information and recommendations to the teacher. ●The teacher delegates specific types of tasks to the technology, for example, giving feedback on a particular math assignment or sending out reminders to students before an assignment is due. ●The teacher delegates more broadly to the technology, with clear protocols for alerts, for monitoring, and for when the teacher takes back control. These and other choices need to be debated openly. For example, we may want to define instructional decisions that have different kinds of consequences for a student and be very careful about delegating control over highly consequential decisions (for example, placement in a next course of study or disciplinary referrals). For human in the loop to become more fully realized, AI technologies must allow teacher monitoring, have protocols to signal a teacher when their judgment is needed, and allow for classroom, school, or district overrides 52 | P a g e Chapter 5: Teaching when they disagree with an instructional choice for their students. We cannot forget that if a technology allows a teacher choice—which it should—it will take significant time for a teacher to think through and set up all the options, requiring greater time initially. 5.6. Challenge: Making Teaching Jobs Easier While Avoiding Surveillance We also recognize that the very technologies that make jobs easier might also introduce new possibilities for surveillance (Figure 9). In a familiar example, when we enable a voice assistant in the kitchen, it might help us with simple household tasks like setting a cooking timer. And yet the same voice assistant might hear things that we intended to be private. This kind of dilemma will occur in classrooms and for teachers. When they enable an AI-assistant to capture data about what they say, what teaching resources they search for, or other behaviors, the data could be used to personalize resources and recommendations for the teacher. Yet the same data might also be used to monitor the teacher, and that monitoring might have consequences for the teacher. Achieving trustworthy AI that makes teachers’ jobs better will be nearly impossible if teachers experience increased surveillance. A related tension is that asking teachers to be “in the loop” could create more work for teachers if not done well, and thus, being in the loop might be in tension with making teaching jobs easier. Also related is the tension between not trusting AI enough (to obtain assistance) or trusting it too much (and incurring surveillance or loss of privacy). For example, researchers have documented that people will follow instructions from a robot during a simulated fire emergency even when 53 | P a g e

Chapter 5: Teaching (a) They are told the robot is broken and (b) the advice is obviously wrong. We anticipate teachers will need training and support to understand how and when they will need to exercise human judgement. Figure 9: Highly customized assistance vs. surveillance increased teacher 5.7. Challenge: Responding to Students’ Strengths While Protecting Their Privacy Educators seek to tackle inequities in learning, no matter how they manifest locally (e.g. in access to educational opportunities, resources, or supports). In culturally responsive and culturally sustaining approaches, educators design materials to build on the “assets”—individual, community, and cultural strengths that students bring to learning. Along with considering assets, of course, educators must meet students where they are, including both strengths and needs. AI could assist in this process by helping teachers with customizing curricular resources, for example. But to do so, the data inputted in an AI-enabled system would have to provide more information about the students. This information could be, but need not be, demographic details. It could also be information about students’ preferences, outside interests, relationships, or experiences. What happens to this data, how it is deleted, and who sees it is of huge concern to educators. As educators contemplate using AI-enabled technologies to assist in tackling educational inequities, they must consider whether the 54 | P a g e Chapter 5: Teaching information about students shared with or stored in an AIenabled system is subject to federal or state privacy laws, such as FERPA. Further, educators must consider whether interactions between students and AI systems create records that must be protected by law, such as when a chatbot or automated tutor generates conversational or written guide to a student. Decisions made by AI technologies, along with explanations of those decisions that are generated by algorithms may also be records that must be protected by law. Therein, a third tension emerges, between more fully representing students and protecting their privacy (Figure 10). Figure 10: Responding to students’ strengths while fully protecting student privacy Further, representation would be just a start toward a solution. As discussed earlier in this report, AI can introduce algorithmic discrimination through bias in the data, code, or models within AI- enhanced edtech. Engineers develop the pattern detection in AI models using existing data, and the data they use may not be representative or may contain associations that run counter to policy goals. Further, engineers shape the automations that AI implements when it recognizes patterns, and the automations may not meet the needs of each student group with a diverse population. The developers of AI are typically less diverse than the populations they serve, and as a consequence, they may not anticipate the ways in which pattern detection and automation may harm a community, group, or individual. 55 | P a g e

Chapter 5: Teaching AI could help teachers to customize and personalize materials for their students, leveraging the teacher’s understanding of student needs and strengths. It is time consuming to customize curricular resources, and teachers are already exploring how AI chatbots can help them design additional resources for their students. An elementary school teacher could gain powerful supports for changing the visuals in a storybook to engage their students or for adapting language that poorly fits local manners of speaking or even for modifying plots to incorporate other dimensions of a teacher’s lesson. In the Learning section, we noted that AI could help identify learner strengths. For example, a mathematics teacher may not be aware of ways in which a student is making great sense of graphs and tables about motions when they are in another teacher’s physics classroom and might not realize that using similar graphs about motion could help with their linear function lesson. AI might help teachers when they seek to reflect student strengths by creating or adapting instructional resources. Yet, the broad equity challenges of avoiding algorithmic discrimination while increasing community and cultural responsiveness must be approached within the four foundations we earlier outlined: human in the loop, equity, safety and effectiveness, and evaluation of AI models. We cannot expect AI models to respect cultural responsiveness. The Department is particularly concerned that equity is something that engaged educators and other responsive adults are in the best position to address and something that is never solely addressable as a computational problem. 5.8. Questions worth Asking About AI for Teaching 56 | P a g e Chapter 5: Teaching As leaders in both pre-service and post-service teacher education contemplate how AI can improve teaching (along with policymakers, developers, and researchers), we urge all in the ecosystem to spend more time asking these questions: • Is AI improving the quality of an educator’s day-to-day work? Are teachers experiencing less burden and more ability to focus and effectively teach their students? • As AI reduces one type of teaching burden, are we preventing new responsibilities or additional workloads being shifted and assigned to teachers in a manner that negates the potential benefits of AI? • Is classroom AI use providing teachers with more detailed insights into their students and their strengths while protecting their privacy? • Do teachers have oversight of AI systems used with their learners? Are they exercising control in the use of AIenabled tools and systems appropriately or inappropriately yielding decision-making to these systems and tools? • When AI systems are being used to support teachers or to enhance instruction, are the protections against surveillance adequate? • To what extent are teachers able to exercise voice and decision-making to improve equity, reduce bias, and increase cultural responsiveness in the use of AI-enabled tools and systems? 5.9. Key Recommendation: Inspectable, Explainable, Over ridable AI 57 | P a g e

Chapter 5: Teaching In the Introduction, we discuss the notion that when AI is incorporated into a system, the core of the AI is a model. In the Learning section, we discuss that we need to be careful that models align to the learning we envision (e.g., that they aren’t too narrow). Now, based on the needs of teachers (as well as students and their families/caregivers), we add another layer to our criteria for good AI models: the need for explainability. Some AI models can recognize patterns in the world and do the right action, but they cannot explain why (e.g., how they arrived at the connection between the pattern and the action). This lack of explainability will not suffice for teaching; teachers will need to know how an AI model analyzed the work of one of their students and why the AI model recommended a particular tutorial, resource, or next step to the student. Thus, explainability of an AI system’s decision is key to a teacher’s ability to judge that automated decision. Such explainability helps teachers to develop appropriate levels of trust and distrust in AI, particularly to know where the AI model tends to make poor decisions. Explainability is also key to a teacher’s ability to monitor when an AI system may be unfairly acting on the wrong information (and thus may be biased. We discuss bias and fairness more in the Assessment section next). Surrounding the idea of explainability is the need for teachers to be able to inspect what an AI model is doing. For example, what kinds of instructional recommendations are being made and to which students? Which students are being assigned remedial work in a never ended loop? Which are making progress? Dashboards in current products present some of this information, but with AI, teachers may want to further explore which decisions are being made and for whom and know of the student-specific factors that an AI model had available (and 58 | P a g e Chapter 5: Teaching possibly which factors were influential) when reaching a particular decision. For example, some of today’s adaptive classroom products use limited recommendation models that only consider student success on the last three mathematics problems and do not consider other variables that a teacher would know to consider, such as whether a student has an IEP Plan or other needs. Our call for attending to equity considerations as we evaluate AI models requires information about how discriminatory bias may arise in particular AI systems and what developers have done to address it. This can only be achieved with transparency for how the tools use datasets to achieve outcomes and what data they have available or that a teacher could include in her judgement but are not available to the system (IEP status is offered as an example above). Teachers will also need the ability to view and make their own judgement about automated decisions, such as decisions about which set of mathematics problems a student should work on next. They need to be able to intervene and override decisions when they disagree with the logic behind an instructional recommendation. Teachers need protection against adverse ramifications when they assert human judgement over an AI system’s decision. “These systems sometimes are seen as a black box kind of a situation where predictions are made based on lots of data. But what we need is to have a clear view—to clearly show how those recommendations or those interactions are made and what evidence is used or what data is used to be able to make those recommendations so teachers and everyone involved know about why that kind of system is providing that type of 59 | P a g e

Chapter 5: Teaching information. So, having open learning environments or inspectable learner models or applications where the stakeholders can understand how these systems make decisions or recommendations is going to be an important aspect in the future of teaching and learning.” —Diego Zapata-Rivera 60 | P a g e Chapter 6: Formative Assessment 6. Formative Assessment Formative assessment is traditionally a key use of edtech because feedback loops are vital to improving teaching and learning. As we have emphasized throughout this report, a top priority with AI is to keep humans in the loop and in control, which includes focusing on the people engaged with formative assessments: students, teachers, school leaders, families/caregivers, and others who support learners. In the definition below, please note the overlap between definitions of AI and formative assessment; both have to do with detecting patterns and choosing a future course of action (that adapts to learner strengths and needs). Assessment refers to all those activities undertaken by teachers, and by the students in assessing themselves, which provide information to be used as feedback to modify the teaching and learning activities in which they are engaged. Such assessment becomes “formative assessment” when the evidence is actually used to adapt the teaching to meet the needs. 6.1. Building on Best Practices A number of dimensions hold potential for shaping the future of formative assessments, and many have ready extensions to the field of AI-enabled systems and tools. For example, the 2017 NETP discussed how technology can lead to improved formative assessments along seven dimensions, listed below: 1. Enabling Enhanced Question Types: To give students more ways to show what they know and can do. 2. Measurement of Complex Competencies: To better elicit growth in important skills that go beyond typical subject matter standards, for example, in measuring practices, social skills like teamwork, selfregulation, and 61 | P a g e

Chapter 6: Formative Assessment Work-relevant skills (e.g., making presentations or leading teams). 3. Providing Real-Time Feedback: To maintain and increase student engagement and to support effective learning, providing timely and helpful responses and suggestions to each learner. 4. Increasing Accessibility: To include neurodiverse learners and to engage learners’ best communication capabilities as they share what they know and can do. 5. Adapting to Learner Ability and Knowledge: To make assessments more precise and efficient. 6. Embedded Assessment in the Learning Process: To emphasize an assessment’s role in improving teaching and learning (this report does not focus on assessment for accountability purposes). 7. Assess for Ongoing Learning: To reveal progress over time and not just predetermined milestones. AI models and AI-enabled systems may have potential to strengthen formative assessments. In one example, a question type that invites students to draw a graph or create a model can be analyzed with AI algorithms, and similar student models might be grouped for the teacher to interpret. Enhanced formative assessment may enable teachers to better respond to students’ understanding of a concept like “rate of change” in a complex, real-world situation. AI can also give learners feedback on complex skills, such as learning American Sign Language or speaking a foreign language and in other practice situations where no person is available to provide immediate feedback. 62 | P a g e Chapter 6: Formative Assessment Generally, an AI assistant may be able to reduce the load for teachers related to grading simpler aspects of student responses, allowing the teacher to focus their specialized judgment on important qualities of a whole essay or a complex project. We also may be able to better provide feedback with accessibility. For example, an AI-enabled learning technology may be able to interact verbally with a student about their response to an essay prompt, asking questions that guide the student to clarify their argument without requiring the student to read a screen or type at a keyboard. In the examples shared earlier in the Learning section, we also see that AI can be embedded in the learning process, providing feedback to students as they work to solve a problem, rather than only later after the student has reached a wrong answer. When formative assessment is more embedded, it can better support learning, and timely feedback is critical. Although there are many points of connection like these between AI and formative assessments, our listening sessions also revealed attendees’ desire to tackle some existing shortcomings in the field of formative assessment; namely, the time-consuming and sometime onerous nature of taking tests, quizzes, or other assessments and the lack of perceived value in the feedback loop by teachers and students. 6.2. Implications for Teaching and Learning Real-time instructional feedback can be beneficial when it helps learners and teachers to improve. But common experience too often leaves students and teachers with unpleasant feelings toward assessment and thus poses a provocative conflict between the potential benefits of data collected through formative assessments and the practical implications of administering additional assessments in classrooms and schools. 63 | P a g e

Chapter 6: Formative Assessment Some AI-enabled systems and tools seek to address this potential conflict. For example, one AI- enabled reading tutor listens to students as they read aloud and provides on-the-spot feedback to improve their reading. Students reportedly enjoyed reading aloud, and the approach was effective. Researchers have also embedded formative assessments in games so that students can show how well they understand Newtonian physics as they play increasingly difficult levels of a game. If a student can more easily ask for and receive help when they feel frustrated or confused, reducing those feelings can feel encouraging. Student feelings of safety, confidence, and trust in the feedback generated by these AI-enabled systems and tools are essential to showcase their learning. That focus on learning growth and gains is optimal (absent negative consequences or a high-stakes environment). AI-enhanced formative assessments may have the potential to save teachers’ time (e.g., time spent on grading), allowing the instructor to spend more time engaged in helping students. AIenhanced assessments may also benefit teachers if they provide detailed insights about student strengths or needs that may not be visible and if they support instructional adaptation or improvement by suggesting a small set of evidence-based recommendations for helping students master content. Such assessments may also be helpful outside of the classroom if it can provide feedback when the teacher is not available, for example, in completing homework or practicing a concept during study hall. As we discussed in the Teaching section, an essential aspect of deploying AI-based formative assessment must be centering teachers in system design. 6.3. Insight: AI Can Enhance Feedback Loops 64 | P a g e Chapter 6: Formative Assessment The term “formative assessment” does not singularly mean a test or a measurement. Assessment becomes formative when it results in useful reflections and changes to the course of teaching, learning, or both. The term “feedback loops” emphasizes that measurement is only part of the process. Feedback loops that lead to instructional improvement— including adaptations in teaching and learning—yield the strongest outcomes for students. We also use “feedback loops” as a plural term because there are many types and levels of loops that are important. Students can benefit from feedback when they work individually, as a member of a small group, or in a classroom discussion. Feedback loops are valuable “in the moment”—for example, as a student practices a skill. Further, feedback loops are valuable when they cover larger spans of effort and reflections, such as at the end of presenting a project or term paper. In addition, feedback loops can assist teachers, for example, helping them notice their own patterns of responding to students’ ideas. Moreover, feedback loops are critical to the continuous improvement of products and the implementation of programs. Due to the importance of feedback loops, formative assessment could be a leading area for schools’ explorations of powerful uses of AI in teaching and learning. Educators can build upon alignments between their long-standing visions for formative assessment and the emerging capabilities that AI holds. Further, the professional assessment community brings a toolkit for asking and answering questions about topics like bias and fairness. The psychometric toolkit of methods is a strong start toward the questions that must be asked and answered because it already contains ways to measure bias and fairness and, more generally, to benchmark the quality of formative assessments. 65 | P a g e

Chapter 6: Formative Assessment But as our discussion reveals, AI can only make feedback loops better if we keep a firm eye on the weaknesses of AI and how AI introduces new concerns. 6.4. An Example: Automated Essay Scoring One instructive example is Automated Essay Scoring (AES). To become strong writers, which is a valuable life skill, students need regular and specific feedback. However, reviewing and providing feedback on essays is very time consuming for humans. Hence, Ellis Page provided a first vision for computer programs that could review and provide feedback on student essays in 1966, and much effort has gone into AES technologies in the intervening 56 years. Many research review articles are available to summarize the progress, which has been impressive. Further, some of today’s applications of AES technologies will be familiar to readers, such as Grammarly, Turnitin, and the various essay analysis engines used by publishers and assessment companies. Also note that while the traditional AES functionality emphasizes scoring or rating essays, newer AI-enabled products focus more on providing students with constructive criticism and developing their skills as writers. Writing is a life skill that is important to the pursuit of college and career ambitions, and developing writers require comprehensive feedback. If developers could inexpensively augment human feedback to developing writers with AI feedback, it’s possible that support for learning to write could become more equitable. And yet, AES is an instructive example because researchers have analyzed limitations, too. AES technologies in AI can analyze some features of student essays but can also be misled by the length of an essay, by a student who places appropriate 66 | P a g e Chapter 6: Formative Assessment keywords in sentences that don’t make sense, and other flaws that a human reader would easily notice. In a telling quote, one team that reviewed the state of the art wrote this: “Nevertheless, the time when AES systems will be able to operate on a par with human judges, with similar levels of connoisseurship for such features as meaning, emotion, originality, creativity, fluency, sense of audience and so on, arguably remains a long way off.” —Gardner, O’Leary, and Yuan The authors further note that while human and AI judgements of essays may correlate, people and computers are not noticing the same things in student writing. Due to these limitations, we must continue to emphasize a human in the loop foundation for AI-enhanced formative assessment. AI may support but not replace high-quality, human-led processes and practices of formative assessment in schools. 6.5. Key Opportunities for AI in Formative Assessment Based on the listening sessions we held, we see three key areas of opportunity in supporting formative assessment using AI systems and models. First, we recommend a strong focus on measuring what matters and particularly those things that have not been easily measured before and that many constituents would like to include in feedback loops. The example above, AES, was chosen because writing remains a valuable academic, workplace, and life skill. Looking at community goals through the lens of their visions for their high school graduates, we see that families/caregivers, students, and community leaders want to nurture graduates who solve problems adaptively, who communicate and collaborate well, who persevere and self-regulate when they experience 67 | P a g e

Chapter 6: Formative Assessment challenges. “What matters” today reaches beyond a sole focus on the core academic content measured by large-scale summative assessments, to support students and teachers with actionable feedback that nurtures the broader skills students need to succeed and thrive. Further, within core academic content, AI may help us to provide feedback on the more realistic and complex aspects of doing math, for example, or investigating scientific phenomena, understanding history, or discussing literature. Second, we’d like to see a strong focus on improving helpseeking and help-giving. Asking for and giving help is crucial to learning and practicing a growth-mindset and central to the notion of human feedback loops. Students may not always know when they need help. In one example, computer algorithms can detect a student who is “wheel spinning” (working hard on mastering content but not making progress).A student who is working hard may not feel like they need help, and the teacher may not be aware that the student is struggling if he or she appears to be “on task.” AI may also be helpful by highlighting for students and teachers what forms of assistance have been most useful to the student in the recent past so that an educator can expand access to specific assistance that works for that individual student. Finally, educators may learn things from AI-enabled systems and tools that give feedback and hints during the completion of homework, utilizing that feedback to later reinforce concepts in direct instruction and strengthen the one-on-one support provided to students. AIenabled systems and tools can provide teachers with additional information about the students’ recent work, so their instructor has a greater contextual sense as they begin to provide help. 68 | P a g e Chapter 6: Formative Assessment Third, we advocate for teachers and students to be strongly involved in designing feedback loops as developers produce AIenhanced formative assessments so they can directly voice what would make assessments less onerous and more convenient and valuable to them. Earlier in the Teaching section, we emphasized how important it is to involve teachers in designing, selecting, and evaluating AI-enhanced technologies. Students need to be centered, too. They are experiencing AI in their everyday lives, and they have strong opinions on what is valuable and safe. There are local and cultural variations in how people provide and receive feedback, so adjusting feedback to align with community norms is important. 6.6. Key Recommendation: Harness Assessment Expertise to Reduce Bias Bias and fairness are important issues in assessment design and administration, and they hold relevance for the area of AIenabled assessment. In traditional assessment, a test item might be biased if unnecessary details are included that differentially advantage some students (e.g., a story-based item that references a sport that only boys play regularly may be less helpful to girls). As discussed earlier, with AI, we now must worry about algorithmic discrimination which can arise due to the manner in which AI algorithms are developed and improved from large datasets of parameters and values that may not represent all cohorts of learners. Algorithmic discrimination is not just about the measurement side of formative assessment; it is also about the feedback loop and the instructional interventions and supports that may be undertaken in response to data collected by formative assessments. There is a question both about access to such interventions and the quality or appropriateness of such 69 | P a g e

Chapter 6: Formative Assessment interventions or supports. When an algorithm suggests hints, next steps, or resources to a student, we have to check whether the help-giving is unfair because one group systematically does not get useful help which is discriminatory. Fairness goes beyond bias as well. In AI-enabled formative assessment, both the opportunity to learn through feedback loops, as well as the quality of learning in and outside of such loops, should be addressed. Issues of bias and fairness have arisen in traditional assessments, and the field of psychometrics has already developed valuable tools to challenge and address these issues. Assessment as a field may have a head start on tackling bias and fairness for AI in education. And yet the issues expand with AI, so the work is not done. Strong and deliberate attention to bias and fairness is needed as future formative assessments are developed. 6.7. Related Questions As indicated, formative assessment is an area in which AI is expanding along a continuum that can be guided by visions already in place, such as the 2017 NETP. It is an area in which AI is poised to grow, especially with capabilities that power more feedback loops in student learning. As this growth takes place, we suggest ongoing attention to the following questions: ● Is formative assessment bringing benefits to the student learning experience and to the efficacy of classroom instruction? ● Are humans being centered in AI-enabled formative assessment and feedback loops? ● Are we providing empowering professional development to teachers so they can leverage feedback loops and safeguard against concerns? 70 | P a g e Chapter 6: Formative Assessment ● To what extent are the developers and implementers of AIenabled systems and tools tackling new sources of algorithmic bias and continuing to make assessment fairer? ● Are governance policies regarding who owns, controls, and can view or use AI-enabled formative assessment data appropriate and adequate? ● Do we have sufficient guardrails against misuse of formative assessment data or automatically generated interpretations of student achievement and learning, such as on dashboards? ● Is trust in an AI-enabled assessment system, feedback loops, and data generated by such assessments growing or diminishing? 71 | P a g e

Chapter 7: Research and Development 7. Research and Development Policy relies upon research-based knowledge; likewise, improving practice depends on feedback loops that analyze empirical evidence. Consequently, the 2010 NETP specified a series of “grand challenges” which were “R&D problems that might be funded and coordinated at a national level.” One 2010 NETP grand challenge was to create personalized learning systems that continuously improve as they are used: “Design and validate an integrated system that provides real-time access to learning experiences tuned to the levels of difficulty and assistance that optimize learning for all learners and that incorporates selfimproving features that enable it to become increasingly effective through interaction with learners.” Since 2010, much R&D has addressed this challenge. Conferences about learning analytics, educational data mining, and learning at scale have blossomed. Developers have created platforms that use algorithms and the analysis of big data to tune learning experiences. The challenge has not been fully achieved, and further work on this challenge is still relevant today. 7.1. Insight: Research Can Strengthen the Role of Context in AI Despite the relevance of 2010’s grand challenges, it has become apparent that the R&D community is now looking to expand their attention. The 2010 challenges were stated as technical problems. Today’s researchers want to more deeply investigate context, and today’s tech companies want to develop platforms that are responsive to the learners’ characteristics and situations more broadly—not just in terms of narrow cognitive attributes. We see a push to transform R&D to address context sensitivity. 72 | P a g e Chapter 7: Research and Development We look forward to new meanings of “adaptive” that broaden outward from what the term has meant in the past decade. For example, “adaptive” should not always be a synonym of “individualized” because people are social learners. Researchers therefore are broadening “adaptivity” to include support for what students do as they learn in groups, a form of learning that is prevalent in schools across the U.S. The focus on context is not an accident. Context is a traditional challenge in AI. Thus, researchers and developers are wise to prioritizing context. Unless we invest more in AI that is contextsensitive, it is quite likely that AI will break and fail to achieve educational goals. Agreeing to prioritize context won’t be easy. As illustrated above in Figure 12, there will be a tension between depth of context and pace of technological advances in AI R&D. On the one hand, AI is sometimes presented as a race to be the first to advance new techniques or scale new applications—innovation is sometimes portrayed as rapidly going to scale with a minimally viable product, failing fast, and only after failure, dealing with context. On the other hand, researchers and developers see that achieving good innovations with AI in education will clearly require bringing more context into the process early and often. For example, researchers highlight that humans must be continually adjusting the goals for technology and have noted that when we set forth goals, we often don’t yet fully understand context; and as we learn about context, the goals must change. This suggests that context must be prioritized early and habitually in R&D; we don’t want to win a race to the wrong finish line. Figure 12: The tension between depth of context and pace of technological advances in AI 73 | P a g e

Chapter 7: Research and Development Further, intensifying focus on context in this work will change the nature of the R&D. There won’t be just one type of change in R&D because context has multiple meanings. Attendees in our listening sessions described four types of context necessary for the future. We list these four types of context below and then expand on each one in its own section. These four types emerged as topics of provocations to think differently about R&D but certainly do not exhaust the important ways of investigating context. 1. Focus on the Long Tail: How could we use big data and AI to pay more attention to the “long tail” of edtech use— going beyond a few “most typical” ways of using emerging technology and instead solving for digital equity and inclusion? 2. Partnership in Design-Based Research: How can we change who is involved and influential in designing the future of AI in education to more centrally include students, teachers, and other educational constituents? 3. Connect with Public Policy: How can work on AI in education build on general advances in AI ethics, safety, and regulation and contribute additional advances specific to educational policy? 74 | P a g e Chapter 7: Research and Development 4. Rethink Teacher Professional Development: How can we solve for new systems of teacher professional development (both pre-service and in-service) that align to the increasingly core role of technology in the teaching profession? “We can't necessarily always apply traditional research methodologies to this topic because educational technology changes so quickly.” —Kristina Ishmael, Office of Educational Technology 7.2. Attention to the Long Tail of Learner Variability At the core of R&D of AI in education, innovators will be building models that fit available data. The increasing scale and prevalence of technologies means that the data is coming from and including a wide range of different contexts and varied ways that people in those contexts engage in teaching and learning. Researchers in our listening sessions drew attention to the promise of AI for addressing “context” by reference to the long tail of learner variability. Figure 13: The long tail of learner variability 75 | P a g e

Chapter 7: Research and Development As depicted in Figure 13, learners vary in their strengths and needs. The most frequently occurring mix of strength and needs (also known as “teaching to the middle”) is depicted leftmost, with less frequently occurring mixes spreading to the right. Rising upward, the figure depicts the number of learners who benefit from a particular learning design, pathway, or approach. We argue that AI can bring opportunities to address a wider spectrum of strengths and needs but only if developers and innovators focus on the long tail and not only “teaching to the middle.” For the sake of argument, the figure indicates three zones. In a first zone, curricular resources are mostly standardized, with perhaps a dimension or two of adaptivity. For example, many existing products adapt based on the correctness of student answers and may also provide options to read or hear text in a second language. However, the core of the instructional approach is highly standardized. In a second zone, there is greater balance between how much standardization and how much adaptivity students can access. Universal Design for Learning (UDL) is one set of recommendations for providing learning opportunities in multiple formats and for accommodating different learning progressions. UDL can enable accommodating more ways in which learners vary, and as teachers know, there are many more important ways to adapt to students than found in today’s edtech products. Students are neurodiverse. They bring different assets from their experiences at home, in their communities, and in their cultures. They have different interests and motivations. And they learn in varied settings—classrooms and schools differ, and at-home students learn in informal settings in ways that 76 | P a g e Chapter 7: Research and Development could complement school learning. These are all important dimensions of “context.” Zone 3 indicates highly adaptive learning, where standardization is less successful and where we need to discover a wider variety of approaches to engage learners and sustain powerful learning. Researchers in our listening sessions noted the promise of Zone 3 because AI’s ability to recognize patterns in data can extend beyond the most common patterns and because AI’s ability to generate customized content can extend beyond what people can reasonably generate on their own. Notice that although the Zone 1 bar appears to be the tallest, and thus tends to attract initial attention, there are more students in Zones 2 and 3, the regions where AI can provide more help. Thus, it’s important to ask where AI researchers and developers are directing their attention. When we say a model “fits,” are we saying it fits the most common and typical uses by teachers and learners? This sort of R&D is easier to do. However, machine learning and AI also can tailor a model to the less common and more culturally specific contexts, too. Therefore, how can constituents cultivate interdisciplinary expertise to direct attention among researchers and developers to focus on the long tail? If we do, the quality of what we do for those represented in that tail can be more adaptive and more context-sensitive. And to be most effective, it will require the integration of contextual, content, and technical expertise. Within the long-tail challenge, the community is wondering how we can get to research insights that are both general and specific enough. When research produces very general abstractions about learning, it often doesn’t give developers enough guidance on exactly how to adjust their learning environments. Conversely, when research produces a specific 77 | P a g e

Chapter 7: Research and Development adaptive algorithm that works on one educational platform, it often remains hard to apply to additional platforms; research can be too detailed as well. The research community is also thinking about new partnerships that could bring more data and more diverse perspectives to the table, the topic of the next section. Focusing on the long tail of learner variability is particularly important to addressing a long- standing key research question: “Do new AI-enhanced approaches work to improve learning, and for whom and under what conditions?” 7.3. Partnership in Design-Based Research Of course, teachers must be included in rethinking their own professional development. This thought leads to another priority aspect of context: partnership in design-based research. With regard to inclusive design, attendees in our listening sessions brought up a variety of co-design and other participatory processes and goals that can be used in R&D. By co-design, they mean sharing power with nonresearchers and non-developers through all the phases of design and development, which would result in more influence by teachers, students, and other constituents in the shape of AIenabled edtech. The shift toward co-design was palpable throughout our listening sessions, but as researchers and developers have not standardized on one particular co- design method, we share some representative examples. ● Youth can powerfully participate in design when researcher methods include participant co-design. Such research can investigate how to improve edtech while educating students. A listening session attendee asked about developing 78 | P a g e Chapter 7: Research and Development students’ awareness of what data are being collected and how data are being used by developers. ● There is a near future need to go beyond representation so that co-designed solutions consider more generous contexts for broader possibilities, according to attendees. ● The shift of power dynamics is another researchworthy interest of the panel and attendees to understand the balance between a teacher’s agency and a machine’s suggestions. ● Likewise, such longitudinal research will require both the infrastructure and institutional support to fund necessary experimentation and requisite failures to elicit positive results and safe innovation. ● There is a desire for rapid cycle evaluations with inclusive feedback loops that return to the educators themselves as essential relative to traditional research approaches. ● Many researchers also mentioned a focus on explainable AI as essential to enable participation in the design and evaluation of emerging AI approaches in education. The conversations raised this question: how can co-design provide an empowering form of participation in design and thus achieve digital inclusion goals? Such digital inclusion can span many layers of design, including diverse representation in design of policies around data, design of adaptivity, and other user experiences in AI systems, design of plans for cultivating AI literacy for users of new platforms, and lastly, the design of plans to evaluate systems. 7.4. Re-thinking Teacher Professional Development 79 | P a g e

Chapter 7: Research and Development With regard to teachers as professionals, both researchers and other educators attending our listening sessions were highly concerned about the disconnect between how teachers are prepared versus how they are expected to work with emerging technology. When we discuss learning, teachers are central actors, and thus the contexts in which they are prepared is centrally important to their ability to do great work in current and emerging technological environments. Teacher professional development, professional learning, and leadership (PD or PL) for emerging technologies was seen as an area needing intense re-thinking, and research could lead the way. Today, few who prepare to become a teacher in an established pre-service program learn about the effective use of educational technology in schools and classrooms; those who do have the opportunity to investigate technology rarely think about the structures that shape its use in the classroom and in educational leadership. Consequently, a troubling dichotomy arises between a small set of investigators who specifically consider educational technology in their research on teaching and a broader group of educators who see educational technology as a generic instructional resource. The challenge is high because teacher professional development will remain highly varied by local contexts. Yet insufficient attention to teachers as leaders in the use and further development of effective educational technology is widespread in teacher professional development research. One response can be in terms of investigating how to nurture greater AI literacy for all teachers. AI literacy is not only important to protect educators and students from possible dangers but also valuable to support teachers to harness the good and do so in innovative ways. A panelist reminded the 80 | P a g e Chapter 7: Research and Development group that this work implies how we prepare educators with a baseline AI literacy and understanding. More transparency and authentic dialogue can foster trust, which was mentioned by a researcher as a chief concern for all teachers and students. This is not to suggest that AI literacy is a complete or even a simple fix. Researchers want to ask fundamental questions about what it means for teachers to be professionals, especially as emerging technologies gain ground in schools and classrooms—our teachers’ professional workplaces. Researchers want to broadly reconceptualize teacher professionalism and to stop treating technology as an add-on element of professional development. 7.5. Connecting with Public Policy Defining human-centered AI for education requires the embrace of a human-centered principle and foundation for developing and formulating policies that govern the application and use of AI more generally throughout society. For example, power dynamics that arise between companies and consumers in society around issues like data ownership will also arise in the education-specific ecosystem. Further, the public discourse in which people are discussing ethics, bias, responsibility, and many other necessary concepts will be happening simultaneously in public policy and in educational ecosystems. One clear implication in our listening sessions was that efforts to improve AI literacy in education could be important and helpful to society more generally. For example, one panelist said that an overarching goal of improving AI literacy is necessary if they are to contribute to how those technologies are designed. Another researcher was interested in how edtech can 81 | P a g e

Chapter 7: Research and Development provide environments where students can experience having difficult discussions across perspectives, an issue which is endemic to present society. A third researcher noted the insufficiencies of prior efforts to contend with algorithmic bias, ethics, and inclusion due to a classroom’s complex social dynamics. Researchers want to take a lead in going beyond checkbox approaches to take these issues seriously. And they also acknowledge that engaging with policy is often a new form of context for edtech and AI researchers, many of whom don’t have long experiences in policy arenas. Likewise, developers often do have experience with some policy issues, such as data privacy and security, but are now needing to become part of new conversations about ethics, bias, transparency, and more, a problem that the EdSAFE AI Alliance is addressing through multi- sector working groups and policy advocacy. 7.6. Key Recommendation: Focus R&D on Addressing Context Attendees who have participated in listening sessions leading up to this report were exceptionally clear that their view of future R&D involved a shift from narrow technical questions to richer contextual questions. This expansive shift toward context, as detailed below, is the foundational orientation that the listening session attendees saw as being necessary to advancing R&D. Attendees included these as dimensions of context: • Learner variability, e.g., in disabilities, languages spoken, and other relevant characteristics; • Interactions with peers, teachers, and others in the learning settings; 82 | P a g e Chapter 7: Research and Development • Relationships across home, school, and community settings, including cultural assets; • Instructional resources available while learning; • Teacher preparation; and • Policies and systems that structure teaching and learning. To more fully represent the context of teaching and learning, including these and other dimensions of text, researchers will have to work in partnership with others to understand which aspects of context are most relevant to teaching and learning and how they can be usefully incorporated into AI models. 7.7. Ongoing Questions for Researchers As mentioned earlier, people are good at context; AI —not so much. R&D investment in context- rich edtech thus could serve multiple national interests because finding ways to do a better job with context would be a fundamental advancement in AI. Indeed, questions like these reverberate across all applications of AI in society, and education is a centrally good context for investigating them: ● Are AI systems moving beyond the tall portions of the “long tail” to adapt to a greater range of conditions, factors, and variations in how people learn? ● To what extent are AI technologies enhancing rather than replacing human control and judgment of student learning? ● How will users understand the legal and ethical implications of sharing data with AI enabled technologies and how to mitigate privacy risks? ● To what extent does technology account for the complex social dynamics of how people work and learn together, or is technology leading humans to narrow or oversimplify? 83 | P a g e

Chapter 7: Research and Development ● How can we more clearly define what we mean by a context-sensitive technology in terms that are both concrete and broad enough? How can we measure it? ● To what extent are technical indicators and human observations of bias or unfairness working together with human observations? How can concerns about ethics and equity in AI technologies become actionable both in R&D, and later, when AI is widely used? ● Are we learning for whom and under what conditions AI systems produce desired benefits and impacts and avoid undesirable discrimination, bias, or negative outcomes? 7.8. Desired National R&D Objectives Attendees sought immediate progress on some key R&D issues, such as these: • Clarifying and achieving a consensus on the terms that go beyond data privacy and data security, including ideas like human-centered, value-sensitive, responsible, ethical, and safe so constituents can advocate for their needs meaningfully and consistently • Creating and studying effective programs for AI literacy for students, teachers, and educational constituents in general, including literacy with regard to the ethics and equity issues specific to AI in educational settings • Advancing research and development to increase fairness, accountability, transparency, and safety in AI systems used in educational settings • Defining participatory or co-designed research processes that include educators in the development and conduct of 84 | P a g e Chapter 7: Research and Development research related to the development, use, and efficacy of AIenabled systems and tools • Highlighting and advancing R&D efforts that empower the participation and voices of youth regarding research, data, and design of AI applications for teaching and learning Longer term desires for a national R&D program include some of the following objectives: • Funding sustainable partnerships that uncover what context means and how it can be addressed over longer periods of time • Better connecting goals for “broadening participation” (for example, in STEM learning pathways) to strategies for addressing learner variability and diversity • Prioritizing research to revitalize support for instructors in light of the increasingly technological nature of K12, higher education, and workplace learning settings • Creating infrastructure and new ways of working together beyond individual field- initiated grants so that R&D with big data and leveraging emerging AI capabilities becomes safer and more productive 85 | P a g e

Chapter 8: Recommendations 8. Recommendations Earlier, we asked two guiding questions: 1. What is our collective vision of a desirable and achievable educational system that leverages automation while protecting and centering human agency? 2. On what timeline will we be ready with necessary guidelines and guardrails along with convincing evidence of positive impacts, so that we can ethically and equitably implement this vision widely? Answers to the first question are provided throughout the Learning, Teaching, Assessment, and Research sections. This section turns to a call to action to education leaders and to recommendations. Core to the Department’s perspective is that education will need leadership specific to our sector. Leadership should recognize and build on prior accomplishments in edtech (such as strong prior work on student privacy and school data security) as well as broad frameworks for safe AI (such as the Blueprint for an AI Bill of Rights). Leadership must also reach beyond these accomplishments and frameworks to address emerging opportunities and risks that are specific to novel capabilities and uses of AI in education. 8.1. Insight: Aligning AI to Policy Objectives Individual sections of this policy report provided insights in each of four areas—learning, teaching, assessment, and research. These insights, synthesized from extensive stakeholder consultation and listening sessions, show that the advances in AI can bring opportunities to advance the Department’s policy objectives: 86 | P a g e Chapter 8: Recommendations ● In support of our objective of attracting and retaining teachers, our nation could focus on AI assistants that make teaching jobs better and provide teachers with the information they need to work closely and empathically with students. An emphasis on teachers in the loop could ensure that AI-enabled classroom technologies keep teachers in the know, in touch with their students, and in control of important instructional decisions. Keeping the teacher in the loop is important to managing risks, as well. ● In support of equitable learning, especially for those most affected by the pandemic, AI could shift edtech from a current deficit-based model to a strengths-based alternative. In addition to finding student weaknesses and assigning fixes, edtech could make recommendations based on strengths that students bring to learning and how adapting to the whole student—a cognitive, social, and self-regulating person—could enable more powerful learning. Adapting to the whole student should include supporting students with disabilities as well as English learners. With regard to equity, we must remain highly attuned to the challenges of bias (which are inherent to how AI systems are developed) and take firm action to ensure fairness. ● With regard to growth trajectories to successful careers, AI-enabled assessments could provide students and teachers with formative guidance on a wider range of valuable skills, focusing on providing information that enhances learning. Aligned with the human-centric view, we should take a systems view of assessments where students, teachers, and others remain at the center of instructional decision making. 87 | P a g e

Chapter 8: Recommendations ● With regard to equity, as research advances and brings more context into AI, we will be better able to use AI to support goals that require customization of learning resources, such as enabling teachers to more easily transform materials to support neurodiverse learners and increase responsiveness to local communities and cultures. Going forward, educational leaders need to bring these and their own policy priorities to the table at every discussion about AI, driving the conversation around human priorities and not only their excitement about what new technology might do. Fundamentally, AI seeks to automate processes that achieve goals, and yet, AI should never set goals. The goals must come from educators’ vision of teaching and learning and educators’ understanding of students’ strengths and needs. 8.2. Calling Education Leaders to Action We summarize seven recommendations for policy action. These recommendations are for education leaders. In the introduction, we note the necessity of involving education constituents in determining policies for AI. We also observed throughout our listening sessions that people coming from many different roles in education all have passion, knowledge, and insights to contribute. In our view, all types of constituents can be education leaders. We are reluctant to suggest any constituent role is more important to advance any of the recommendations, but we call out specific needs for action within some of the recommendations where it is warranted. 8.3. Recommendation #1: Emphasize Humans in the Loop We start with a central recommendation throughout this report. This recommendation was a clear constituent favorite. Indeed, 88 | P a g e Chapter 8: Recommendations across more than 700 attendees in our listening sessions, the predominant discussion tackled how constituents can achieve a consensus vision for AI-enabled edtech where humans are firmly at the center. The Blueprint for an AI Bill of Rights similarly calls for “access to timely human consideration and remedy by a fallback and escalation process if an automated system fails, it produces an error, or you would like to appeal or contest its impacts…” Building on this consensus, we call upon all constituents to adopt “humans in the loop” as a key criterion for educational use of AI. We envision a technology-enhanced future more like an electric bike and less like robot vacuums. On an electric bike, the human is fully aware and fully in control, but their burden is less, and their effort is multiplied by a complementary technological enhancement. Robot vacuums do their job, freeing the human from involvement or oversight. Although teachers should not be the only humans involved in loops, Figure 5 provided examples of three types of teacher loops that are central to education and can be used to illustrate what “human in the loop” means. Here, we use the example of an AI chatbot to elaborate on the meaning of the loops. First, as students become involved in extended interactions with AI chatbots, teachers will need to educate students about safe AI use, monitor their use, and provide human recourse when things go astray. Second, teachers are beginning to use chatbots to plan personalized instruction for their students; they will need to be involved in loops with other teachers to understand effective prompts, to know how to analyze AI-generated lesson plans for flaws, and to avoid the human tendency to overly trust AI systems and underapply human judgement. Third, teachers need to be involved in the design and evaluation of AI systems before 89 | P a g e

Chapter 8: Recommendations they are used in classrooms and when needs for improvement are observed. In one example, to design AI-generated homework support for students, teachers’ in-depth understanding of the cognitive, motivational, and social supports their students need will provide much-needed guidance as a homework-support chatbot is designed. In framing AI in education, this report advances a key recommendation of “human in the loop” AI because the phrase readily communicates a criterion that everyone can use as they determine which AI-enabled systems and tools are appropriate for use in teaching and learning. In a rather technical field, human in the loop is an approachable and humanistic criterion. Rather than suggesting that AI-enabled systems and tools should replace teachers, this term instead solidifies the central role of educators as instructors and instructional decision makers, while reinforcing the responsibility of teachers to exercise judgement and control over the use of AI in education. It resonates with the important idea of feedback loops, which are highly important to how people teach and learn. It also aligns with the ideas of inspectable, explainable, severable, and over ridable AI. The Department agrees with listening session participants who argued that teachers should not be the only humans in the loop and calls upon parents, families, students, policy makers, and system leaders to likewise examine the “loops” for which they are responsible, critically analyze the increasing role of AI in those loops, and determine what they need to do to retain support for the primacy of human judgement in educational systems. 90 | P a g e Chapter 8: Recommendations 8.4. Recommendation #2: Align AI Models to a Shared Vision for Education “All models are wrong, but some are useful.” —George Box, Statistician As we have discussed across every section of this report, AI technologies are grounded in models, and these models are inevitably incomplete in some way. It is up to humans to name educational goals and measure the degree to which models fit and are useful—or don’t fit and might be harmful. Such an assessment of how well certain tools serve educational priorities may seem obvious, but the romance of technology can lead to a “let’s see what the tech can do'' attitude, which can weaken the focus on goals and cause us to adopt models that fit our priorities poorly. Here we call upon educational policy and decision makers at the local, state, and federal level to use their power to align priorities, educational strategies, and technology adoption decisions to place the educational needs of students ahead of the excitement about emerging AI capabilities. We want to strengthen their attention to existing state, district, and schoollevel policies that guide edtech adoption and use, such as the four levels of evidence in ESSA, the privacy requirements of FERPA, and enhanced policies to come. Local education leaders know best what their urgent educational priorities are. Every conversation about AI (or any emerging technology) should start with the educational needs and priorities of students front and center and conclude with a discussion about the evaluation of effectiveness re-centered on those needs and priorities. Equity, of course, is one of those priorities that requires constant attention, especially given the worrisome consequences of potentially biased AI models. 91 | P a g e

Chapter 8: Recommendations We especially call upon leaders to avoid romancing the magic of AI or only focusing on promising applications or outcomes, but instead to interrogate with a critical eye how AI-enabled systems and tools function in the educational environment. We ask leaders to distrust broad claims and ask six types of questions, listed below. Throughout this report, we elaborated on which characteristics of AI model use in education are most important to evaluate for alignment to intended educational goals. To aid leaders, we summarize our insights about AI models and their use in educational tools and systems in Figure 14. Figure 14: Recommendation for desired qualities of AI tools and systems in education In this figure, we center teaching and learning in all considerations about the suitability of an AI model for an 92 | P a g e Chapter 8: Recommendations educational use. Humans remain in the loop of defining, refining, and using AI models. We highlight the six desirable characteristics of AI models for education (elaborating from principles in the Blueprint for an AI Bill of Rights to fit the specifics of educational systems): 1. Alignment of the AI Model to Educators’ Vision for Learning: When choosing to use AI in educational systems, decision makers prioritize educational goals, the fit to all we know about how people learn, and alignment to evidencebased best practices in education. 2. Data Privacy: Ensuring security and privacy of student, teacher, and other human data in AI systems is essential. 3. Notice and Explanation: Educators can inspect edtech to determine whether and how AI is being incorporated within edtech systems. Educators’ push for AI models can explain the basis for detecting patterns and/or for making recommendations, and people retain control over these suggestions. 4. Algorithmic Discrimination Protections: Developers and implementers of AI in education take strong steps to minimizing bias and promoting fairness in AI models. 5. Safe and Effective Systems: The use of AI models in education is based on evidence of efficacy (using standards already established in education for this purpose) and work for diverse learners and in varied educational settings. 6. Human Alternatives, Consideration and Feedback: AI models that support transparent, accountable, and responsible use of AI in education by involving humans in 93 | P a g e

Chapter 8: Recommendations the loop to ensure that educational values and principles are prioritized. Although we first address our recommendation to interrogate how educational systems use AI models to educational leaders who adopt technologies, other leaders also have integral roles to play. Teachers and students, as well as their families/caregivers, contribute significantly to adoption decisions also. And leaders and parents must support educators when they question or override an AI model based on their professional wisdom. Developers of technologies need to be forthcoming about the models they use, and we may need policymakers to create requirements for disclosure so that the marketplace can function on the basis of information about AI models and not only by the claims of their benefits. We also emphasize the need for a government role. AI models are made by people and are only an approximation to reality. Thus, we need policies that require transparency about the AI models that are embedded in educational systems, as well as models that are inspectable, explainable, and over ridable. Our listening sessions featured constituent calls for government doing more to hold developers accountable for disclosing the types of AI models they employ in large-scale products and the safeguards included in their systems. Government leaders can make a positive contribution to market conditions that enable building trust as AI systems are procured and implemented in education. We discuss these guidelines more in recommendation #4, which is about building trust. 8.5. Recommendation #3: Design Using Modern Learning Principles 94 | P a g e Chapter 8: Recommendations We call for the R&D sector to ensure that product designs are based on best and most current principles of teaching and learning. The first decade of adaptivity in edtech drew upon many important principles, for example, around how to sequence learning experiences and how to give students feedback. And yet the underlying conception was often deficitbased. The system focused on what was wrong with the student and chose pre-existing learning resources that might fix that weakness. Going forward, we must harness AI’s ability to sense and build upon learner strengths. Likewise, the past decade of approaches was individualistic, and yet we know that humans are fundamentally social and that learning is powerfully social. Going forward, we must build on AI capabilities that connect with principles of collaborative and social learning and which respect the student not just for their cognition but also for the whole human skill set. Going forward, we also must seek to create AI systems that are culturally responsive and culturally sustaining, leveraging the growth of published techniques for doing so. Further, most early AI systems had few specific supports for students with disabilities and English learners. Going forward, we must ensure that AI-enabled learning resources are intentionally inclusive of these students. The field has yet to develop edtech that builds upon each student’s ability to make choices and to self-regulate in increasingly complex environments. We have to develop edtech that expands students’ abilities to learn in creative modes and to expand their ability to discuss, write, present, and lead. We also call upon educators to reject uses of AI that are based solely on machine learning from data—without triangulation based on learning theory and knowledge from practice. Achieving effective and equitable educational systems requires 95 | P a g e

Chapter 8: Recommendations more than processing “big data,” and although we want to harness insights from data, human interpretation of data remains highly important. We reject a technological determinism in which patterns in data, on their own, tell us what to do. Applications of AI in education must be grounded in established, modern learning principles, the wisdom of educational practitioners, and should leverage the expertise in the educational assessment community around detecting bias and improving fairness. 8.6. Recommendation #4: Prioritize Strengthening Trust Technology can only help us to achieve educational objectives when we trust it. Yet, our listening sessions revealed the ways in which distrust of edtech and AI is commonplace. Constituents distrust emerging technologies for multiple reasons. They may have experienced privacy violations. The user experience may be more burdensome than anticipated. Promised increases in student learning may not be backed by efficacy research. They may have experienced unanticipated consequences. Unexpected costs may arise. Constituents may distrust complexity. Trust needs to incorporate safety, usability, and efficacy. The Department firmly takes the stance that constituents want AI that supports teachers and rejects AI visions that replace teachers. And yet, teachers, students, and their families/caregivers need support to build appropriate levels of trust in systems that affect their work. In the broader ecosystem, trustworthy AI is recognized as a multidimensional problem (including the dimensions of Figure 14, above). If every step forward does not include strong elements of trust building, we worry that distrust will distract from innovation serving the public good that AI could help realize. 96 | P a g e Chapter 8: Recommendations We expect that associations and societies have a key role in strengthening trust. Some important associations like the State Educational Technology Directors Association and the Consortium for School Network work with edtech leaders, and parallel organizations like EDUCAUSE work with postsecondary leaders. Other associations and societies work with teachers, education leaders, and education staff developers. Industry networks, like the EdSAFE AI Alliance, can bring together industry leaders to work together to foster trust. Additional societies bring researchers together. These societies and associations have the reach necessary to bring all parts of the educational ecosystem into discussions about trust and also the ability to represent the views of their constituents in crosscutting policy discussions. 8.7. Recommendation #5: Inform and Involve Educators Our listening sessions also asked for more specific direction on the question of what education leaders should do (see Figure 15). The most frequent responses fit three clusters: the need for guidelines and guardrails, strengthening the role of teachers, and re-focusing research and development. These are activities that constituents are asking for and that could expand trust. The recommendations that follow respond to these requests. Figure 15: Listening session attendees prioritized involving practitioners, research, and evaluation and the need for guidelines and guardrails. 97 | P a g e

Chapter 8: Recommendations In particular, one concern that repeatedly arose in our listening sessions was the potential for AI to result in less respect for educators or less value for their skills. Across the nation, we are now responding to decreasing interest in entering or remaining in the teaching profession. Now is the time to show the respect and value we hold for educators by informing and involving them in every step of the process of designing, developing, testing, improving, adopting, and managing AI-enabled edtech. This includes involving educators in reviewing existing AI-enabled systems, tools, and data use in schools, designing new applications of AI based on teacher input, carrying out pilot evaluations of proposed new instructional tools, collaborating with developers to increase the trustworthiness of the deployed system, and raising issues about risks and unexpected consequences as the system is implemented. 98 | P a g e Chapter 8: Recommendations We have already seen educators rise to the challenge of creating overall guidelines, designing specific uses of available AIenabled systems and tools, and ferreting out concerns. And yet, the influence of educators in the future of AI-enabled products cannot be assumed; instead, constituents need policies that put muscle behind it. Could we create a national corps of leading educators representing every state and region to provide leadership? Could we commit to developing necessary professional development supports? Can we find ways to compensate educators so they can be at the forefront of designing the future of education? Our policies should enable educators to be closely involved in design of AI-enabled educational systems. Although we know that the responsibility for informing and involving educators must be distributed at all levels of national and school governance, the Office of Educational Technology can play a key role in informing and involving educators through its reports, events, outreach, and in a future NETP. Although examples above refer to K-12 teachers, higher education instructors must also be included. We also call on the edtech industry to involve educators throughout their design and development processes. For example, AI-enabled teaching assistants are only likely to help teachers do their job if teachers are thoroughly involved as the assistants are designed. We call upon institutions that prepare teachers to integrate technology more systematically into their programs; for example, the use of technology in teaching and learning should be a core theme across teacher preparation programs, not an issue that arises only in one course. 8.8. Recommendation #6: Focus R&D on Addressing Context and Enhancing Trust and Safety 99 | P a g e

Chapter 8: Recommendations Research that focuses on how AI-enabled systems can adapt to context (including variability among learners) in instructional approaches and across educational settings is essential to answering the question of, “Do specific applications of AI work in education, and if so, for whom and under what conditions?” The italicized phrase points to variability among learners and diversity in the settings for learning. We call upon innovators in R&D to focus their efforts to advance AI on the long tail of learning variability, where large populations of students would benefit from customization of learning. We also call on R&D to lead by establishing how trust can be strengthened in AI-enabled systems, building on the Blueprint’s call for safe and effective systems yet also including education-specific requirements, such as how teachers can be meaningfully involved in design phases, not only in implementation and evaluation. Although many products today are adaptive, some adapt on just one or a few dimensions of variability, such as student’s accuracy in problem solving. As teachers know, there are many more important ways to adapt to students’ strengths and needs. Students are neurodiverse and may have specific disabilities. They bring different assets from their experiences at home, in communities, and in their cultures. They have different interests and motivations. They are in different places in their journeys to master the English language. And they learn in varied settings. Classrooms and schools are different, and at home, students learn in informal settings in ways that could complement school learning. We recommend attention to “context” as a means for expressing the multiple dimensions that must be considered when elaborating the phrase “for whom and under what conditions.” We also acknowledge the role of researchers in conducting evaluations, which must now consider 100 | P a g e Chapter 8: Recommendations not only efficacy but must also explore where harm may arise and the system problems that can occur through weak trust or over-trust in AI systems. R&D must take the lead in making AI models more contextsensitive and ensuring that they are effective, safe, and trustworthy for use with varied learners in diverse settings. Although AI has capabilities to find patterns beyond the limited number of variables that people normally think about, AI is not particularly good at understanding and working with context in the ways people do. Over time, we’ve seen learning sciences grow to be less about individualistic cognitive principles and more encompassing first of social learning and then of the many dimensions of context that matter in learning. Our use of AI needs to follow this trajectory toward context to support educational applications. To achieve human-centric vision, listening session attendees argued that teams will need time and freedom to explore how best to manage the tension between the pace of technological advancement and the need for broader contextual insights—for trust and for safety. They will need time and freedom to pioneer new processes that better involve teachers and students as codesigners, with attention to balancing power dynamics. And they will need to shift attention from older ways of framing priorities (such as achievement gaps) to new ways of prioritizing digital equity. We call on R&D funders to focus resources on the long tail of learner variability, the need for AI-enabled systems that better incorporate context, and time required to get contextual considerations right. We call upon researchers and developers to prioritize challenges of context, trust, and safety in their work to advance AI. 101 | P a g e

Chapter 8: Recommendations 8.9. Recommendation #7: Develop Education-Specific Guidelines and Guardrails Our final recommendation is central to policymakers. A feature of the American educational system is the emphasis on local decision making. With technology growing in complexity at such a rapid pace, it is becoming difficult for local leaders to make informed decisions about the deployment of artificial intelligence. As we have discussed, the issues are not only data privacy and security but extend to new topics such as bias, transparency, and accountability. It will be harder to evaluate promising edtech platforms that rely on AI systems against this evolving, complex set of criteria. Regulations related to key student and family data privacy laws like the Family Educational Rights & Privacy Act (FERPA), the Children’s Internet Privacy Act (CIPA), and the Children’s Online Privacy Protection Act (COPPA) warrant review and further consideration in light of new and emerging technologies in schools. Laws such as the Individuals with Disabilities Education Act (IDEA) may likewise be considered as new situations arise in the use of AI-enabled learning technologies. As discussed throughout this document, the Blueprint for an AI Bill of Rights is an important framework throughout this work. The Department encourages parallel work by constituents in all levels of the educational system. In addition to the key federal laws cited immediately above, many states have also passed privacy laws that govern the use of educational technology and edtech platforms in classrooms. Further constituents can expect general frameworks for responsible AI in parallel sectors like health, safety, and consumer products to be informative but not sufficient for education’s specific needs. Leaders at every level need awareness of how this work reaches beyond implications 102 | P a g e Chapter 8: Recommendations for privacy and security (e.g., to include awareness of potential bias and unfairness), and they need preparation to effectively confront the next level of issues. 8.10. Next Steps We are heartened to see intensifying discussions throughout the educational ecosystem about the role of AI. We see progress that we can build upon occurring, as constituents discuss these three types of questions: What are the most significant opportunities and risks? How can we achieve trustworthy educational AI? How can we understand the models at the heart of applications of AI and ensure they have the qualities that align to educational aspirations? The Department developed this report with awareness of contributions arising from many types of organizations and collectives. Internationally, we recognize parallel efforts to consider AI in the European Union, at the United Nations, and indeed throughout the world. We are aware of progress being led by organizations such as UNESCO, the EdSAFE AI Alliance, and research organizations in many countries. We plan to continue cross-agency work, for example, by continuing to coordinate with the Office of Science and Technology Policy and other Federal agencies as agencies implement next steps guided by the Blueprint for an AI Bill of Rights. We see a broad and fertile context for necessary next steps: ● Working within this context and with others, the Department will consider specific policies and regulations so that educators can realize the opportunities of AI in edtech while minimizing risks. For example, the Department is developing a set of AI usage scenarios to strengthen the process of evaluating and enhancing policies 103 | P a g e

Chapter 8: Recommendations and regulations. The principles and practices in the Blueprint for an AI Bill of Rights will be used to ensure the scenarios mitigate important risks and harms. ● Working with constituents (including education leaders; teachers, faculty, support staff, and other educators; researchers; policymakers; funders; technology developers; community members and organizations; and above all, learners and their families/caregivers), we will develop additional resources and events to increase understanding of AI and to involve those who will be most affected by these new technologies. ● Working across sectors, such as education, innovation, research, and policy, we will revise and update the NETP to guide all constituents toward safe, equitable, and effective AI in education in the United States, in alignment with our overall educational priorities. 104 | P a g e Common Acronyms and Abbreviations 9. Common Acronyms and Abbreviations ⚫ AES: Automated Essay Scoring ⚫ AI: Artificial Intelligence ⚫ CIPA: Children’s Internet Protection Act ⚫ COPPA: Children’s Online Privacy Protection Act ⚫ Edtech: Educational Technology ⚫ ESEA: Elementary and Secondary Education Act ⚫ ESSA: Every Student Succeeds Act ⚫ FERPA: Family Educational Rights and Privacy Act ⚫ IA: Intelligence Augmentation ⚫ IDEA: Individuals with Disabilities Education Act ⚫ IEP: Individualized Education Program ⚫ ITS: Intelligent Tutoring Systems ⚫ NETP: National Education Technology Plan ⚫ R&D: Research & Development 105 | P a g e

Acknowledgements 10. Acknowledgements Project Team Artificial Intelligence and the Future of Teaching and Learning was developed under the leadership and guidance of Roberto J. Rodríguez, Assistant Secretary for the Office of Planning, Evaluation, and Policy Development, Kristina Ishmael, Deputy Director of the Office of Educational Technology, and Bernadette Adams, Senior Policy Advisor for the Office of Educational Technology at the U.S. Department of Education. Support for the creation of this document was provided by Digital Promise, led by Jeremy Roschelle with Carly Chillmon, Judi Fusco, Gabrielle Lue, Eric Nentrup, My Nguyen, Pati Ruiz, and Zohal Shah. Special thanks to Center for Integrative Research in Computing and Learning Sciences postdocs Michael Chang and Aditi Mallavarapu. Listening Session Panelists and Hosts Hal Abelson Ryan Baker Nancye Blair Black Worsley Michael Chang Carly Chillmon Sherice Clarke Tammy Clegg Sidney d’Mello Judi Fusco Dragan Gasevic Sarah Hampton Kristina Ishmael Jim Larimore Nicol Turner Lee Sherry Loftin Gabrielle Lue Aditi Mallavarap u Ole Vidula Jeremy Pati Ruiz Alina Von Erin Walker Diego We also thank 1,075 people who registered for Listening Sessions and 700 who attended. 106 | P a g e References 11. References Akgun, S., Greenhow, C. (2022). Artificial intelligence in education: Addressing ethical challenges in K-12 settings. AI Ethics, 2, 431–440. https://doi.org/10.1007/s43681-02100096-7 Aleven, V., McLaughlin, E. A., Glenn, R. A., & Koedinger, K. R. (2016). Instruction based on adaptive learning technologies. In Mayer, R.E. & Alexander, P.A., Handbook of research on learning and instruction, 522-560. ISBN: 113883176X Baker, R.S., Esbenshade, L., Vitale, J., & Karumbaiah, S. (2022). Using demographic data as predictor variables: A questionable Black, P. & Wiliam, D. (1998). Inside the black box: Raising standards through classroom https://doi.org/10.35542/osf.io/y4wvj assessment. Phi Delta Kappan, 92(1), 81-90. https://kappanonline.org/inside-the-black- box-raisingstandards-through-classroom-assessment/ Black, P., & Wiliam, D. (2009). Developing the theory of formative assessment. Educational Assessment, Evaluation and Accountability, https://doi.org/10.1007/s11092-0089068-5 Boden, M.A. (2018). Artificial intelligence: A very short introduction. Oxford. ISBN: 978-0199602919 Bryant, J., Heitz,C., Sanghvi, S., & Wagle, D. (2020, January 14). How artificial intelligence will impact K-12 teachers. McKinsey. https://www.mckinsey.com/industries/education/ourinsights/how-artificial-intelligence-will-impact-k-12teachers Celik, I., Dindar, M., Muukkonen, H. & Järvelä, S. (2022). The promises and challenges of artificial intelligence for teachers: A systematic review of research. TechTrends, 66, 616– 630. https://doi.org/10.1007/s11528-022-00715-y Center for Integrative Research in Computing and Learning Sciences (CIRCLS). (2022, Feb.). From Broadening to empowering: Reflecting on the CIRCLS’21 convening. https://circls.org/circls21report 107 | P a g e 21(1), 5-31. choice.

References Chen, C., Park, H.W. & Breazeal, C. (2020). Teaching and learning with children: Impact of reciprocal peer learning with a social robot on children’s learning and emotive engagement. Computers & Education, https://doi.org/10.1016/j.compedu.2020.103836 Chen, G., Clarke, S., & Resnick, L.B. (2015). Classroom Discourse Analyzer (CDA): A discourse analytic tool for teachers. Technology, Instruction, Cognition and Learning, 10(2), 85-105 Dieterle, E., Dede, C. & Walker, M. (2022). The cyclical ethical effects of using artificial intelligence in education. AI & Society. https://link.springer.com/article/10.1007/s00146022-01497-w Doewes, A. & Pechenizkiy, M. (2021). On the limitations of human-computer agreement in automated essay scoring. In Proceedings of the 14th International Conference on Educational Data Mining (EDM21). https://educationaldatamining.org/EDM2021/virtual/static /pdf/EDM21_paper_243.pdf Englebart, D.C. (October 1962). Augmenting human intellect: A conceptual framework. SRI Summary Report https://www.dougengelbart.org/pubs/augment-3906.html Ersozlu, Z., Ledger, S., Ersozlu, A., Mayne, F., & Wildy, H. (2021). Mixed-reality learning environments in teacher education: An analysis of TeachLivETM Research. SAGE Open, 11(3). https://doi.org/10.1177/21582440211032155. European Commission, Directorate-General for Education, Youth, Sport and Culture. (2022). Ethical guidelines on the use of artificial intelligence (AI) and data in teaching and learning for educators. Publications Office of the European Union. https://data.europa.eu/doi/10.2766/153756 Forsyth, S., Dalton, B., Foster, E.H., Walsh, B., Smilack, J., & Yeh, T. (2021, May). Imagine a more ethical AI: Using stories to develop teens' awareness and understanding of artificial intelligence and its societal impacts. In 2021 Conference on Research in Equitable and Sustained Participation in Engineering, Computing, and Technology (RESPECT). Friedman, L., Blair Black, N., Walker, E., & Roschelle, J. (November 8, 2021) Safe AI in education https://doi.org/10.1109/RESPECT51740.2021.9620549 108 | P a g e IEEE. AFOSR-3223. 150, References needs you. Association of Computing Machinery BLOG@ACM, https://cacm.acm.org/blogs/blog-cacm/256657-safe-ai-ineducation-needs-you/fulltext Gardner, J., O'Leary, M. & Yuan, L. (2021). Artificial intelligence in educational assessment: "Breakthrough? Or buncombe and ballyhoo?" Journal of Computer Assisted Learning, 37(5), 1207–1216. https://doi.org/10.1111/jcal.12577 Gartner (n.d.) Gartner glossary: Augmented intelligence. Gartner. https://www.gartner.com/en/informationtechnology/glossary/augmented-intelligence Gay, G. (2018). Culturally responsive teaching: Theory, research, and practice. Teachers College Press. ISBN: 978-0807758762 Godwin-Jones, R. Opportunities and challenges. Language Learning & Technology, http://hdl.handle.net/10125/44747 (2021). Big data and language learning: 25(1), 4–19. Hammerness, K., Darling-Hammond, L., & Bransford, J. (2005). Preparing teachers for a changing world: What teachers should learn and be able to do. Jossey-Bass. ISBN: 0787996343 Holmes, W. & Porayska-Pomsta, K. (Eds.) (2022). The ethics of artificial intelligence in education. Routledge. ISBN 978-0367349721 Holstein, K., McLaren, B.M., & Aleven, V. (2019). Co-designing a real-time classroom orchestration tool to support teacher– AI complementarity. Journal of Learning Analytics, 6(2). https://doi.org/10.18608/jla.2019.62.3 IEEE-USA Board of Directors. http://globalpolicy.ieee.org/wpcontent/uploads/2017/10/IEEE17003.pdf Jensen, E., Dale, M., Donnelly, P.J., Stone, C., Kelly, S., Godley, A. & D'Mello, S.K. (2020). Toward automated feedback on teacher discourse to enhance teacher learning. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (CHI https://doi.org/10.1145/3313831.3376418 Kai, S., Almeda, M.V., Baker, R. S., Heffernan, C., & Heffernan, N. (2018). Decision tree modeling of wheel-spinning and productive persistence in skill builders. Journal of Educational 109 | P a g e '20). intelligence research, development and regulation. (February 10, 2017). Artificial IEEE

References Data Mining, 10(1), https://doi.org/10.5281/zenodo.3344810 Kaplan, R.M., & Saccuzzo, D.P. (2017). Psychological testing: Principles, applications, and issues. Cengage Learning. Ke, Z., & Ng, V. (2019). Automated essay scoring: A survey of the state of the art. In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 6300–6308. https://doi.org/10.24963/ijcai.2019/879 Khosravi, H., Shum, S.B., Chen, G, Conati, C., Tsai,Y-S., Kay, J., Knight, S., Martinez-Maldonado, R., Sadiq, S., Gašević, D. (2022). Explainable artificial intelligence in education. Computers and Education: Artificial Intelligence, https://doi.org/10.1016/j.caeai.2022.100074 Kulik, J.A., & Fletcher, J.D. (2016). Effectiveness of intelligent tutoring systems: A meta-analytic review. Review of Educational Research, 86(1), 42–78 Ma, W., Adescope, O.O, Nesbit, J.C. & Liu, Q. (2014). Intelligent tutoring systems and learning outcomes: A meta-analysis. Journal of Educational Psychology, 106(4), 901–918. http://dx.doi.org/10.1037/a0037123 Maslej, N., Fattorini, L., Brynjolfsson E., Etchemendy, J., Ligett, K., Lyons, T., Manyika, J., Ngo, H., Niebles, J.C., Parli, V., Shoham, Y., Wald, R., Clark, J. and Perrault, R., (2023). The AI index 2023 annual report. Stanford University: AI Index Steering Committee, Institute for Human-Centered AI. Merrill, S. (2020). In schools, are we measuring what matters? Edutopia. https://www.edutopia.org/article/schools-are-wemeasuring-what-matters Molenaar, I. (2022). Towards hybrid human-AI learning technologies. European Journal of Education, 00, 1–14. https://doi.org/10.1111/ejed.12527 Mostow, J., Aist, G., Burkhead, P., Corbett, A., Cuneo, A., Eitelman, S., Huang, C., Junker, B., Sklar, M.B., & Tobin, B. (2003). Evaluation of an automated reading tutor that listens: Comparison to human tutoring and classroom instruction. Journal of Educational Computing Research, 29(1), 110 | P a g e 3. 36–71. References 61–117. RDCF https://doi.org/10.2190/06AX-QW99-EQ5GMousavinasab, E., Zarifsanaiey, N., R. Niakan Kalhori, S., Rakhshan, M., Keikha, L., & Ghazi Saeedi, M. (2021). Intelligent tutoring systems: A systematic review of characteristics, applications, and evaluation methods. Interactive Learning Environments, 29(1), 142–163. https://psycnet.apa.org/doi/10.1080/10494820.2018.155825 7 National Academies of Sciences, Engineering, and Medicine. 2018. How people learn II: Learners, contexts, and cultures. The National Academies Press. https://doi.org/10.17226/24783 National Research Council. 2000. How people learn: Brain, mind, experience, and school. The National Academies Press. https://doi.org/10.17226/9853 Nentrup, E. (2022). How Policymakers Can Support Educators and Technology Vendors Towards SAFE AI. EdSAFE AI Alliance. https://www.edsafeai.org/post/how-policymakers-cansupport- aied Page, E.B. (1966). The imminence of grading essays by computer. Phi Delta Kappan, 47(5), 238– 243 Paris, D., & Alim, H.S. (Eds.). (2017). Culturally sustaining pedagogies: Teaching and learning for justice in a changing world. Teachers College Press. ISBN: 978-0807758342 Plass, J.L., & Pawar, S. (2020). Toward a taxonomy of adaptivity for learning. Journal of Research on Technology in Education, 52(3), 275–300. https://doi.org/10.1080/15391523.2020.1719943 Regona, Massimo & Yigitcanlar, Tan & Xia, Bo & Li, R.Y.M. (2022). Opportunities and adoption challenges of AI in the construction industry: A PRISMA review. Journal of Open Innovation Technology Market https://doi.org/10.3390/joitmc8010045 of Psychology, Second and Complexity, 8(45). Reynolds, C.R., & Suzuki, L.A. (2012). Bias in psychological assessment: An empirical review and recommendations. Handbook Edition. Ritter, S., Anderson, J.R., Koedinger, K.R. & Corbett, A. (2007). Cognitive Tutor: Applied https://doi.org/10.1002/9781118133880.hop210004 research in mathematics education. Psychonomic Bulletin & Review, 14, 249–255/ 111 | P a g e

References https://doi.org/10.3758/BF03194060 Roll, I., Aleven, V., McLaren, B.M., Koedinger, K.R. (2011). Improving students’ help-seeking and Instruction, 21(2), metacognitive feedback in an intelligent tutoring system, Learning https://doi.org/10.1016/j.learninstruc.2010.07.004. Roschelle, J., Dimitriadis, Y. & Hoppe, U. (2013). Classroom orchestration: Synthesis. Computers & Education, 69, https://doi.org/10.1016/j.compedu.2013.04.010 Roschelle, J., Feng, M., Murphy, R. & Mason, C.A. (2016). Online mathematics homework increases student achievement. AERA Open, 2(4), 1-12. DOI: 10.1177/2332858416673968 Roschelle, J., Penuel, W., & Shechtman, N. (2006). Co-design of innovations with teachers: definition and dynamics. In Proceedings of the 7th International Conference on Learning Sciences, Bloomington, https://doi.dx.org/10.22318/icls2006.606 Rose, D. (2000). Universal design for learning. Journal of Special Education Technology, 15(4), https://doi.org/10.1177/016264340001500407 Ruiz, P. & Fusco, J. (2022). Teachers partnering with artificial intelligence: Augmentation and automation. Digital Promise. https://digitalpromise.org/2022/07/06/teachers-partneringwith-artificial-intelligence-augmentation-and-automation/ Russell, S. (2019). Human compatible: Artificial intelligence and the problem of control. Viking. ISBN 978-0-525-55861-3. Shao, Q., Sniffen, A., Blanchet, J., Hillis, M.E., Shi, X., Haris, T.K., & Balkcom, D. (2020). Teaching American Sign Language in mixed reality. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, https://doi.org/10.1145/3432211 Sharples, M. & Pérez y Pérez, R. (2022). Story machines: How computers have become creative writers. Routledge. ISBN 9780367751951 Shemshack, A., Spector, J.M. (2020) A systematic literature review of personalized learning terms. Smart Learning Environments, 7(33). https://doi.org/10.1186/s40561-02000140-9 112 | P a g e 4(4), 1-27. 47-51. 512-526. skills using 267–280. IN. References Shute, V J. (2008). Focus on formative feedback. Review of Educational Research, 78(1), https://doi.org/10.3102/0034654307313795 Shute, V. J., Ventura, M., & Kim, Y. J. (2013). Assessment and learning of qualitative physics in Newton's Playground. The Journal of Educational Research, 106(6), 423-430. Swiecki, Z., Khosravi, H., Chen, G., Martinez-Maldonado, R., Lodge, J.M., Milligan, S., Selwyn, B. & Gašević,D. (2022). Assessment in the age of artificial intelligence. Computers and Education: Artificial Intelligence, 3. The White House (February 17, 2023). Executive order on further advancing racial equity and support for underserved communities through the federal government. https://www.whitehouse.gov/briefing-room/presidentialactions/2023/02/16/executive- order-on-further-advancingracial-equity The White House (September 8, 2022). Readout of White House listening session on tech accountability.https://www.whitehouse.gov/briefingroom/statementsplatform releases/2022/09/08/readout-of-whitehouse-listening-session-on-tech-platform- accountability/ U.S. Department of Education, Office of Educational Technology (2022). Advancing digital equity for all: Community-based recommendations for developing effective digital equity plans to close the digital divide and enable technology-empowered learning. US Department of Education. U.S. Department of Education, Office of Educational Technology. (2010). Transforming American Education: Learning Powered by Technology. U.S. Department of Education. p. 78 Van Lehn, K. (2011) The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. Educational Psychologist, of 46(4), https://doi.org/10.1080/00461520.2011.611369 Wagner, A.R., Borenstein, J. & Howard, A. (September 2018). Overtrust in the robotics age. Communications the ACM, 61(9), https://doi.org/10.1145/3241365 Walker, E., Rummel, N. & Koedinger, K.R. (2015). Adaptive intelligent support to improve peer tutoring in algebra. 113 | P a g e 22-24. 197-221. 153–189.

References Walton Family Foundation (March 1, 2023). Teachers and students embrace ChatGPT for education. International Journal of Artificial Intelligence in Education, 24, 33–61 https://doi.org/10.1007/s40593-013-0001-9 https://www.waltonfamilyfoundation.org/learning/teach ers-and-students-embrace- chatgpt-for-education Webb, N.M., & Farivar, S. (1994). Promoting helping behavior in cooperative small groups in middle school mathematics. American Educational Research Journal, 31(2), 369–395. https://doi.org/10.3102/00028312031002369 White House Office of Science and Technology Policy (October 2022), Blueprint for an AI bill of rights: Making automated systems work for the American people. The White House Office of Science and Wiggins, G. Technology https://www.ascd.org/el/articles/sevenKeys-to-effective-feedback https://www.whitehouse.gov/ostp/ai-bill-of-rights/ (2015). Seven keys Policy. to effective feedback. ACSD. Winne, P.H. (2021). Open learner models working in symbiosis with self-regulating learners: A research agenda. International Journal of Artificial Intelligence in Education, 31(3), 446-459. https://doi.org/10.1007/s40593-020-00212-4 Zacamy, J. & Roschelle, J. (2022). Navigating the tensions: How could equity-relevant research also be agile, open, and scalable? Digital Promise. http://hdl.handle.net/20.500.12265/159 models. Journal of Research https://doi.org/10.1002/tea.21773 Zhang, H., Lee, I., Ali, S., DiPaola, D., Cheng, Y., & Breazeal, C. (2022). Integrating ethics and career futures with technical learning to promote AI literacy for middle school students: An exploratory study. International Journal of Artificial Intelligence in Education, https://doi.org/10.1007/s40593-022-00293-3 Zhai, X., He, P., Krajcik, J. (2022). Applying machine learning to automatically assess scientific in Science Teaching. 1–35. 114 | P a g e References ABOUT THE AUTHORS Dr. Raja Roy Choudhury, Professor of Practice at Dr. D. Y. Patil B-School, India, is highly qualified in the world of behavioral health and leadership sciences. He holds Ph.D. degrees in Economics and Psychology. He has 36 years of experience in the areas of retail, education, behavioral health and management consulting. Mr. Budha Chandra Singha, Assistant Professor at Dr. D Y Patil B-School, India, is a Post Graduate in Business Management and has 11 years of experience in teaching Quantitative Techniques and Analytics. He has taught at several B-schools in India. 115 | P a g e

Dr. Raja Roy Choudhury: Dr. Raja Roy Choudhury, Professor of Pho Practice at Dr. D. Y. Patil B-School, India, is highly qualified in the world of behavioral health and leadership sciences. He holds Ph.D. degrees in Economics and Psychology. He has 36 years of experience in the areas of retail, education, behavioral health and management consulting. Address: Dr. D. Y. Patil B-School Sr. No. 87-88, Bengaluru-Mumbai Express Bypass, Tathawade, Pune, Maharashtra 411033 E-mail: drrajaroy.choudhury@dpu.edu.in Brief of the Book Mr. Budha Chandra Singha: Mr. Budha Chandra Singha, Assistant Professor at Dr. D Y Patil B-School, India, is a Post Graduate in Business Management and has 11 years of experience in teaching Quantitative Techniques and Analytics. He has taught at several B-schools in India. Address: Dr. D. Y. Patil B-School Sr. No. 87-88, Bengaluru-Mumbai Express Bypass, Tathawade, Pune, Maharashtra 411033 E-mail: budha.chandra@dpu.edu.in Price 650/e-ISBN :- 978-81-963532-3-0

ISBN: 978-81-963532-2-3 (E-BOOK) Software Engineering Prof KEERTHANA P Prof MANASA KN Prof GANGA D BENAL Publisher’sName InternationalSocietyfor Green,SustainableEngineeringandManagement ISO9001:2015 certifiedforResearch,Development&Training An Autonomous Organization, Under Public Trust Act,GovernmentofWest Bengal,India Member The Indian Science Congress Association Indian Statistical Institute Indian Meteorological Society Certificate of Enlistment as Research Organization ,KMC 2023

SOFTWARE ENGINEERING SOFTWARE ENGINEERING By KEERTHANA P Assistant Professor REVA UNIVERSITY MANASA KN Assistant Professor REVA UNIVERSITY GANGA D BENAL Assistant Professor Cambridge Institute of Technology Publisher’s Name International Society for Green, Sustainable Engineering and Management ISO 9001:2015 certified for Research, Development &Training (Certified as Research Organization, Under Section 199 Rule 435/435A Act 1980 Kolkata Municipal Corporation) An Autonomous Organization, Under Public Trust Act, Government of West Bengal, India Member The Indian Science Congress Association Indian Statistical Institute Indian Meteorological Society Certificate of Enlistment as Research Organization, KMC 2023

© International Society for Green, Sustainable Engineering and Management Editor in Chief: Dr.Debaprayag Chaudhuri, Chairman Production Editor: Mrs.Soma Das Chaudhuri Published in India by International Society for Green, Sustainable Engineering and Management 94,Garfa Main Road, Ground Floor, Jadavpur, Kolkata-700 075,West Bengal India Mobile:0091 96 74 76 61 80 Email: isgsem.research.kolkata@gmail.com Website: http://isgsemkolkata.blogspot.com Copyright © 2023.All rights reserved. No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, mechanical or photocopying, recording, or otherwise without the prior permission of the publisher. The society names used in this set are for identification purposes only. 1st Edition: Augut’2023 ISBN : 978-81-963532-2-3 750 Author details Prof. KEERTHANA P, is an Science and Engineering, Be Computer Science and Engin her Master of Engineering in Currently pursuing a Phd in c Assistant Professor at Reva University, Sc ngaluru, India. She has earned a Bachelor eering from Visvesvaraya Technology Univ computer science and engineering from RE computer science and engineering at REVA chool of Computer or of Engineering in versity. She earned EVA University and A University. She is conducting research in the field of Machine Learning and Deep Learning and Artificial Intelligence. Prof. Manasa KN, is an Assist and Engineering, Bengaluru, Science and Engineering f ant Professor at Reva University, School of India. She earned a Bachelor of Enginee rom Nitte Meenakshi Karnataka in 2012. She earn University Visvesvaraya Colle field of Image Processing, Com Institute of Techn omputer Vision, Machine Learning and Deep L f Computer Science eering in Computer nology, Bengaluru, ned her Master of Engineering in Information Technology from ege of Engineering in 2015. She is conductit ng research in the Learning. Prof. Ganga D Benal Bengaluru, , is an India. She earn University in 2021. She is co Learning. Engineering from Atria Institu earned her Master of Tech Assistant professor at Cambridge Institute ed a Bachelor of Engineering in Informa te of Technology (VTU), Bengaluru, Karna hnology in computer te Of Technology , ation Science and ataka in 2019. She science and engineering from REVA onducting research in the field of Machine Learning and Deep

ISBN : 978-81-963532-2-3 (E-Book) SOFTWARE ENGINEERING BY KEERTHANA P Assistant Professor REVA UNIVERSITY MANASA KN Assistant Professor REVA UNIVERSITY GANGA D BENAL Assistant Professor Cambridge Institute of Technology 1 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) SYLLABUS Module I: Introductory concepts: Introduction, definition, objectives, Life cycle – Requirements analysis and specification. Design and Analysis: Cohesion and coupling, Data flow oriented Design: Transform centered design, Transaction centered design. Analysis of specific systems like Inventory control, Reservation system. Module II: Object-oriented Design: Object modeling using UML, use case diagram, class diagram, activity diagram, unified development process. Module III: Implementing and Testing: Programming language characteristics, fundamentals, languages, classes, coding style efficiency. Testing: Objectives, black box and white box testing, various testing strategies, Art of debugging. Maintenance, Reliability and Availability: Maintenance: Characteristics, controlling factors, maintenance tasks, side effects, preventive maintenance – Re Engineering – Reverse Engineering – configuration management – Maintenance tools and techniques. Reliability: Concepts, Errors, Faults, Repair and availability, reliability and availability models. Recent trends and developments. Module IV: Software quality: SEI CMM and ISO-9001. Software reliability and fault-tolerance, software project planning, monitoring, and control. Computer-aided software engineering (CASE), Component model of software development, Software reuse. Text Book: 1. Mall Rajib, Fundamentals of Software Engineering, PHI. 2. Pressman, Software Engineering Practitioner’s Approach, TMH. 2 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) CONTENTS Module 1: 1: Introduction to Software Engineering 2: Software Development Life Cycle- Classical Waterfall Model 3: Iterative Waterfall Model, Prototyping Model, Evolutionary Model 4: Spiral Model 5: Requirements Analysis and Specification 6: Problems without a SRS document, Decision Tree, Decision Table 7: Formal System Specification 8: Software Design 9: Software Design Strategies 10: Software Analysis & Design Tools 11: Structured Design Module 2: 12: Object Modelling Using UML 13: Use Case Diagram 14: Class Diagrams 15: Interaction Diagrams 16: Activity and State Chart Diagram 5 8 14 17 20 22 28 34 38 43 48 56 59 68 72 75 3 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) Module 3: 17: Coding 18: Testing 19: Black-Box Testing 20: White-Box Testing 21: White-Box Testing (cont..) 22: Debugging, Integration and System Testing 23: Integration Testing 24: Software Maintenance 25: Software Maintenance Process Models 26: Software Reliability and Quality Management 27: Reliability Growth Models Module 4: 28: Software Quality 29: SEI Capability Maturity Model 30: Software Project Planning 31: Metrics for Software Project Size Estimation 32: Heuristic Techniques, Analytical Estimation Techniques 33: COCOMO Model 34: Intermediate COCOMO Model Lecture 35: Staffing Level Estimation 36: Project Scheduling 37: Organization Structure 38: Risk Management 39: Computer Aided Software Engineering 40: Software Reuse 41: Reuse Approach Software Engineering Keerthana P, Manasa KN, Ganga D Bengal 80 88 90 91 99 103 109 112 125 134 143 147 154 161 168 171 176 183 191 199 212 217 4

ISBN : 978-81-963532-2-3 (E-Book) MODULE 1 INTRODUCTION TO SOFTWARE ENGINEERING The term software engineering is composed of two words, software and engineering. Software is more than just a program code. A program is an executable code, which serves some computational purpose. Software is considered to be a collection of executable programming code, associated libraries and documentations. Software, when made for a specific requirement is called software product. Engineering on the other hand, is all about developing products, using well-defined, scientific principles and methods. So, we can define software engineering as an engineering branch associated with theprocedures. The outcome of software engineering is an efficient and reliable software product. IEEE defines software engineering as: The application of a systematic, disciplined, quantifiable approach to the development, operation and maintenance of software. We can alternatively view it as a systematic collection of past experience. The experience is arranged in the form of methodologies and guidelines. A small program can be written without using software engineering principles. But if one wants to develop a large software product, then software engineering principles are absolutely necessary to achieve a good quality software cost effectively. 5 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) Without using software engineering principles it would be difficult to develop large programs. In industry it is usually needed to develop large programs to accommodate multiple functions. A problem with developing such large commercial programs is that the complexity and difficulty levels of the programs increase exponentially with their sizes. Software engineering helps to reduce this programming complexity. Software engineering principles use two important techniques to reduce problem complexity: abstraction and decomposition. The principle of abstraction implies that a problem can be simplified by omitting irrelevant details. In other words, the main purpose of abstraction is to consider only those aspects of the problem that are relevant for certain purpose and suppress other aspects that are not relevant for the given purpose. Once the simpler problem is solved, then the omitted details can be taken into consideration to solve the next lower level abstraction, and so on. Abstraction is a powerful way of reducing the complexity of the problem. The other approach to tackle problem complexity is decomposition. In this technique, a complex problem is divided into several smaller problems and then the smaller problems are solved one by one. However, in this technique any random decomposition of a problem into smaller parts will not help. The problem has to be decomposed such that each component of the decomposed problem can be solved independently and then the solution of the different components can be combined to get the full solution. A good decomposition of a problem should minimize interactions among various components. If the different subcomponents are interrelated, then the different components cannot be solved separately and the desired reduction in complexity will not be realized. NEED OF SOFTWARE ENGINEERING The need of software engineering arises because of higher rate of change in user requirements and environment on which the software is working. • Large software - It is easier to build a wall than to a house or building, likewise, as the size of software become large engineering has to step to give it a scientific process. • Scalability- If the software process were not based on scientific and engineering concepts, it would be easier to re-create new software than to scale an existing one. • Cost- As hardware industry has shown its skills and huge manufacturing has lower 6 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) down the price of computer and electronic hardware. But the cost of software remains high if proper process is not adapted. • Dynamic Nature- The always growing and adapting nature of software hugely depends upon the environment in which the user works. If the nature of software is always changing, new enhancements need to be done in the existing one. This is where software engineering plays a good role. • Quality Management- Better process of software development provides better and quality software product. CHARACTERESTICS OF GOOD SOFTWARE A software product can be judged by what it offers and how well it can be used. This software must satisfy on the following grounds: • Operational • Transitional • Maintenance Well-engineered and crafted software is expected to have the following characteristics: Operational This tells us how well software works in operations. It can be measured on: • Budget • Usability • Efficiency • Correctness • Functionality • Dependability • Security • Safety Transitional This aspect is important when the software is moved from one platform to another: 7 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) • Portability • Interoperability • Reusability • Adaptability Maintenance This aspect briefs about how well a software has the capabilities to maintain itself in the ever- changing environment: • Modularity • Maintainability • Flexibility • Scalability In short, Software engineering is a branch of computer science, which uses well-defined engineering concepts required to produce efficient, durable, scalable, in-budget and ontime software products SOFTWARE DEVELOPMENT LIFE CYCLE LIFE CYCLE MODEL A software life cycle model (also called process model) is a descriptive and diagrammatic representation of the software life cycle. A life cycle model represents all the activities required to make a software product transit through its life cycle phases. It also captures the order in which these activities are to be undertaken. In other words, a life cycle model maps the different activities performed on a software product from its inception to retirement. Different life cycle models may map the basic development activities to phases in different ways. Thus, no matter which life cycle model is followed, the basic activities are included in all life cycle models though the activities may be carried out in different orders in different life cycle models. During any life cycle phase, more than one activity may also be carried out. 8 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) THE NEED FOR A SOFTWARE LIFE CYCLE MODEL The development team must identify a suitable life cycle model for the particular project and then adhere to it. Without using of a particular life cycle model the development of a software product would not be in a systematic and disciplined manner. When a software product is being developed by a team there must be a clear understanding among team members about when and what to do. Otherwise it would lead to chaos and project failure. This problem can be illustrated by using an example. Suppose a software development problem is divided into several parts and the parts are assigned to the team members. From then on, suppose the team members are allowed the freedom to develop the parts assigned to them in whatever way they like. It is possible that one member might start writing the code for his part, another might decide to prepare the test documents first, and some other engineer might begin with the design phase of the parts assigned to him. This would be one of the perfect recipes for project failure. A software life cycle model defines entry and exit criteria for every phase. A phase can start only if its phase-entry criteria have been satisfied. So without software life cycle model the entry and exit criteria for a phase cannot be recognized. Without software life cycle models it becomes difficult for software project managers to monitor the progress of the project. Different software life cycle models Many life cycle models have been proposed so far. Each of them has some advantages as well as some disadvantages. A few important and commonly used life cycle models are as follows: • Classical Waterfall Model • Iterative Waterfall Model • Prototyping Model • Evolutionary Model • Spiral Model 1. CLASSICAL WATERFALL MODEL 9 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) The classical waterfall model is intuitively the most obvious way to develop software. Though the classical waterfall model is elegant and intuitively obvious, it is not a practical model in the sense that it cannot be used in actual software development projects. Thus, this model can be considered to be a theoretical way of developing software. But all other life cycle models are essentially derived from the classical waterfall model. So, in order to be able to appreciate other life cycle models it is necessary to learn the classical waterfall model. Classical waterfall model divides the life cycle into the following phases as shown in fig.2.1: Fig 2.1: Classical Waterfall Model Feasibility study - The main aim of feasibility study is to determine whether it would be financially and technically feasible to develop the product. • At first project managers or team leaders try to have a rough understanding of what is required to be done by visiting the client side. They study different input data to the system and output data to be produced by the system. They study what kind of processing is needed to be done on these data and they look at the various constraints on the behavior of the system. • After they have an overall understanding of the problem they investigate the different solutions that are possible. Then they examine each of the solutions in terms of what kind of resources required, what would be the cost of development and what 10 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) would be the development time for each solution. • Based on this analysis they pick the best solution and determine whether the solution is feasible financially and technically. They check whether the customer budget would meet the cost of the product and whether they have sufficient technical expertise in the area of development. Requirements analysis and specification: - The aim of the requirements analysis and specification phase is to understand the exact requirements of the customer and to document them properly. This phase consists of two distinct activities, namely • Requirements gathering and analysis • Requirements specification The goal of the requirements gathering activity is to collect all relevant information from the customer regarding the product to be developed. This is done to clearly understand the customer requirements so that incompleteness and inconsistencies are removed. The requirements analysis activity is begun by collecting all relevant data regarding the product to be developed from the users of the product and from the customer through interviews and discussions. For example, to perform the requirements analysis of a business accounting software required by an organization, the analyst might interview all the accountants of the organization to ascertain their requirements. The data collected from such a group of users usually contain several contradictions and ambiguities, since each user typically has only a partial and incomplete view of the system. Therefore it is necessary to identify all ambiguities and contradictions in the requirements and resolve them through further discussions with the customer. After all ambiguities, inconsistencies, and incompleteness have been resolved and all the requirements properly understood, the requirements specification activity can start. During this activity, the user requirements are systematically organized into a Software Requirements Specification (SRS) document. The customer requirements identified during the requirements gathering and analysis activity are organized into a SRS document. The important components of this document are functional requirements, the nonfunctional requirements, and the goals of implementation. Design: - The goal of the design phase is to transform the requirements specified in the 11 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) SRS document into a structure that is suitable for implementation in some programming language. In technical terms, during the design phase the software architecture is derived from the SRS document. Two distinctly different approaches are available: the traditional design approach and the object-oriented design approach. • Traditional design approach -Traditional design consists of two different activities; first a structured analysis of the requirements specification is carried out where the detailed structure of the problem is examined. This is followed by a structured design activity. During structured design, the results of structured analysis are transformed into the software design. • Object-oriented design approach -In this technique, various objects that occur in the problem domain and the solution domain are first identified, and the different relationships that exist among these objects are identified. The object structure is further refined to obtain the detailed design. Coding and unit testing:-The purpose of the coding phase (sometimes called the implementation phase) of software development is to translate the software design into source code. Each component of the design is implemented as a program module. The end-product of this phase is a set of program modules that have been individually tested. During this phase, each module is unit tested to determine the correct working of all the individual modules. It involves testing each module in isolation as this is the most efficient way to debug the errors identified at this stage. Integration and system testing: -Integration of different modules is undertaken once they have been coded and unit tested. During the integration and system testing phase, the modules are integrated in a planned manner. The different modules making up a software product are almost never integrated in one shot. Integration is normally carried out incrementally over a number of steps. During each integration step, the partially integrated system is tested and a set of previously planned modules are added to it. Finally, when all the modules have been successfully integrated and tested, system testing is carried out. The goal of system testing is to ensure that the developed system conforms to its requirements laid out in the SRS document. System testing usually consists of three different kinds of testing activities: • α – testing: It is the system testing performed by the development team. 12 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) • β –testing: It is the system testing performed by a friendly set of customers. • Acceptance testing: It is the system testing performed by the customer himself after the product delivery to determine whether to accept or reject the delivered product. System testing is normally carried out in a planned manner according to the system test plan document. The system test plan identifies all testing-related activities that must be performed,specifies the schedule of testing, and allocates resources. It also lists all the test cases and the expected outputs for each test case. Maintenance: -Maintenance of a typical software product requires much more than the effort necessary to develop the product itself. Many studies carried out in the past confirm this and indicate that the relative effort of development of a typical software product to its maintenance effort is roughly in the 40:60 ratios. Maintenance involves performing any one or more of the following three kinds of activities: • Correcting errors that were not discovered during the product development phase. This is called corrective maintenance. • Improving the implementation of the system, and enhancing the functionalities of the system according to the customer’s requirements. This is called perfective maintenance. • Porting the software to work in a new environment. For example, porting may be required to get the software to work on a new computer platform or with a new operating system. This is called adaptive maintenance. Shortcomings of the classical waterfall model The classical waterfall model is an idealistic one since it assumes that no development error is ever committed by the engineers during any of the life cycle phases. However, in practical development environments, the engineers do commit a large number of errors in almost every phase of the life cycle. The source of the defects can be many: oversight, wrong assumptions, use of inappropriate technology, communication gap among the project engineers, etc. These defects usually get detected much later in the life cycle. For example, a design defect might go unnoticed till we reach the coding or testing phase. 13 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 9 Once a defect is detected, the occurred and redo some of th correct the defect and its eff development work, it is not p 978-81-963532-2-3 (E-Book) engineers need to go back to the phas e work done during that phase and the ect on the later phases. Therefore, in possible to strictly follow the classical w 2.ITERATIVE WATERFALL MODEL To overcome the major shor the iterative waterfall model. rtcomings of the classical waterfall mo odel, we come up with se where the defect had he subsequent phases to any practical software waterfall model. Fig 3.1: Iterative Waterfall Model Here, we provide feedback p Though errors are inevitable, but it is desirable to detect them in the they occur. If so, this can red paths for error correction as & when de duce the effort to correct the bug. The advantage of this model stage of development which is that there is a working model of the makes it easier to find functional or issues at an early stage of development enables to take corrective budget. The disadvantage with this SDLC model is that it is applicable c Software Engineering Keerthana P, Manasa KN, Ganga D Bengal e system at a very early design flaws. Finding e measures in a limited software development proje ts. This is because it is hard to break a small software system ly to large and bulky on 14 etected later in a phase. he same phase in which

ISBN : 978-81-963532-2-3 (E-Book) into further small serviceable increments/modules. 3. PRTOTYPING MODEL Prototype A prototype is a toy implementation of the system. A prototype usually exhibits limited functional capabilities, low reliability, and inefficient performance compared to the actual software. A prototype is usually built using several shortcuts. The shortcuts might involve using inefficient, inaccurate, or dummy functions. The shortcut implementation of a function, for example, may produce the desired results by using a table look-up instead of performing the actual computations. A prototype usually turns out to be a very crude version of the actual system. Need for a prototype in software development There are several uses of a prototype. An important purpose is to illustrate the input data formats, messages, reports, and the interactive dialogues to the customer. This is a valuable mechanism for gaining better understanding of the customer’s needs: • how the screens might look like • how the user interface would behave • how the system would produce outputs Another reason for developing a prototype is that it is impossible to get the perfect product in the first attempt. Many researchers and engineers advocate that if you want to develop a good product you must plan to throw away the first version. The experience gained in developing the prototype can be used to develop the final product. A prototyping model can be used when technical solutions are unclear to the development team. A developed prototype can help engineers to critically examine the technical issues associated with the product development. Often, major design decisions depend on issues like the response time of a hardware controller, or the efficiency of a sorting algorithm, etc. In such circumstances, a prototype may be the best or the only way to resolve the technical issues. 15 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) A prototype of the actual product is preferred in situations such as: • User requirements are not complete • Technical issues are not clear Fig 3.2: Prototype Model 4. EVOLUTIONARY MODEL It is also called successive versions model or incremental model. At first, a simple working model is built. Subsequently it undergoes functional improvements & we keep on adding new functions till the desired system is built. Applications: • Large projects where you can easily find modules for incremental implementation. Often used when the customer wants to start using the core features rather than waiting for the full software. • Also used in object oriented software development because the system can be 16 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) easily portioned into units in terms of objects. Advantages: • User gets a chance to experiment partially developed system • Reduce the error because the core modules get tested thoroughly. Disadvantages: • It is difficult to divide the problem into several versions that would be acceptable to the customer which can be incrementally implemented & delivered. Fig 3.3: Evolutionary Model 5. SPIRAL MODEL The Spiral model of software development is shown in fig. 4.1. The diagrammatic representation of this model appears like a spiral with many loops. The exact number of 17 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) loops in the spiral is not fixed. Each loop of the spiral represents a phase of the software process. For example, the innermost loop might be concerned with feasibility study, the next loop with requirements specification, the next one with design, and so on. Each phase in this model is split into four sectors (or quadrants) as shown in fig. 4.1. The following activities are carried out during each phase of a spiral model. Fig 4.1: Spiral Model First quadrant (Objective Setting) • During the first quadrant, it is needed to identify the objectives of the phase. • Examine the risks associated with these objectives. Second Quadrant (Risk Assessment and Reduction) • A detailed analysis is carried out for each identified project risk. • Steps are taken to reduce the risks. For example, if there is a risk that the requirements are inappropriate, a prototype system may be developed. Third Quadrant (Development and Validation) • Develop and validate the next level of the product after resolving the identified risks. Fourth Quadrant (Review and Planning) • Review the results achieved so far with the customer and plan the next iteration around the spiral. • Progressively more complete version of the software gets built with each iteration 18 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) around the spiral. Circumstances to use spiral model The spiral model is called a meta model since it encompasses all other life cycle models. Risk handling is inherently built into this model. The spiral model is suitable for development of technically challenging software products that are prone to several kinds of risks. However, this model is much more complex than the other models – this is probably a factor deterring its use in ordinary projects. Comparison of different life-cycle models The classical waterfall model can be considered as the basic model and all other life cycle models as embellishments of this model. However, the classical waterfall model cannot be used in practical development projects, since this model supports no mechanism to handle the errors committed during any of the phases. This problem is overcome in the iterative waterfall model. The iterative waterfall model is probably the most widely used software development model evolved so far. This model is simple to understand and use. However this model is suitable only for well-understood problems; it is not suitable for very large projects and for projects that are subject to many risks. The prototyping model is suitable for projects for which either the user requirements or the underlying technical aspects are not well understood. This model is especially popular for development of the user-interface part of the projects. The evolutionary approach is suitable for large problems which can be decomposed into a set of modules for incremental development and delivery. This model is also widely used for object- oriented development projects. Of course, this model can only be used if the incremental delivery of the system is acceptable to the customer. 19 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) The spiral model is called a meta model since it encompasses all other life cycle models. Risk handling is inherently built into this model. The spiral model is suitable for development of technically challenging software products that are prone to several kinds of risks. However, this model is much more complex than the other models – this is probably a factor deterring its use in ordinary projects. The different software life cycle models can be compared from the viewpoint of the customer. Initially, customer confidence in the development team is usually high irrespective of the development model followed. During the lengthy development process, customer confidence normally drops off, as no working product is immediately visible. Developers answer customer queries using technical slang, and delays are announced. This gives rise to customer resentment. On the other hand, an evolutionary approach lets the customer experiment with a working product much earlier than the monolithic approaches. Another important advantage of the incremental model is that it reduces the customer’s trauma of getting used to an entirely new system. The gradual introduction of the product via incremental phases provides time to the customer to adjust to the new product. Also, from the customer’s financial viewpoint, incremental development does not require a large upfront capital outlay. The customer can order the incremental versions as and when he can afford them. REQUIREMENTS ANALYSIS AND SPECIFICATION Before we start to develop our software, it becomes quite essential for us to understand and document the exact requirement of the customer. Experienced members of the development team carry out this job. They are called as system analysts. The analyst starts requirements gathering and analysis activity by collecting all information from the customer which could be used to develop the requirements of the system. He then analyzes the collected information to obtain a clear and thorough understanding of the product to be developed, with a view to remove all ambiguities and inconsistencies from the initial customer perception of the problem. The following basic questions pertaining to the project should be clearly understood by the analyst in order to obtain a good grasp of the problem: 20 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) • What is the problem? • Why is it important to solve the problem? • What are the possible solutions to the problem? • What exactly are the data input to the system and what exactly are the data output by the system? • What are the likely complexities that might arise while solving the problem? • If there are external software or hardware with which the developed software has to interface, then what exactly would the data interchange formats with the external system be? After the analyst has understood the exact customer requirements, he proceeds to identify and resolve the various requirements problems. The most important requirements problems that the analyst has to identify and eliminate are the problems of anomalies, inconsistencies, and incompleteness. When the analyst detects any inconsistencies, anomalies or incompleteness in the gathered requirements, he resolves them by carrying out further discussions with the end- users and the customers. Parts of a SRS document •The important parts of SRS document are: Functional requirements of the system Non-functional requirements of the system, and Goals of implementation Functional requirements:The functional requirements part discusses the functionalities required from the system. The system is considered to perform a set of high-level functions {f }. The functional view of the system is shown in fig. 5.1. Each function f of the system can be considered as a transformation of a set of input data (ii) to the corresponding set of output data (o). The user can get some meaningful piece of work done using a high-level function. 21 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) Fig. 5.1: View of a system performing a set of functions Nonfunctional requirements:Nonfunctional requirements deal with the characteristics of the system which cannot be expressed as functions - such as the maintainability of the system, portability of the system, usability of the system, etc. Goals of implementation:The goals of implementation part documents some general suggestions regarding development. These suggestions guide trade-off among design goals. The goals of implementation section might document issues such as revisions to the system functionalities that may be required in the future, new devices to be supported in the future, reusability issues, etc. These are the items which the developers might keep in their mind during development so that the developed system may meet some aspects that are not required immediately. Identifying functional requirements from a problem description The high-level functional requirements often need to be identified either from an informal 22 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) problem description document or from a conceptual understanding of the problem. Each high- level requirement characterizes a way of system usage by some user to perform some meaningful piece of work. There can be many types of users of a system and their requirements from the system may be very different. So, it is often useful to identify the different types of users who might use the system and then try to identify the requirements from each user’s perspective. Example: - Consider the case of the library system, where – F1: Search Book function Input: an author’s name Output: details of the author’s books and the location of these books in the library So the function Search Book (F1) takes the author's name and transforms it into book details. Functional requirements actually describe a set of high-level requirements, where each high-level requirement takes some data from the user and provides some data to the user as an output. Also each high-level requirement might consist of several other functions. Documenting functional requirements For documenting the functional requirements, we need to specify the set of functionalities supported by the system. A function can be specified by identifying the state at which the data is to be input to the system, its input data domain, the output data domain, and the type of processing to be carried on the input data to obtain the output data. Let us first try to document the withdraw-cash function of an ATM (Automated Teller Machine) system. The withdraw-cash is a high-level requirement. It has several sub-requirements corresponding to the different user interactions. These different interaction sequences capture the different scenarios. Example: - Withdraw Cash from ATM R1: withdraw cash Description: The withdraw cash function first determines the type of account that the user has and the account number from which the user wishes to withdraw cash. It checks the balance to determine whether the requested amount is available in the account. If enough 23 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) balance is available, it outputs the required cash; otherwise it generates an error message. R1.1 select withdraw amount option Input: “withdraw amount” option Output: user prompted to enter the account type R1.2: select account type Input: user option Output: prompt to enter amount R1.3: get required amount Input: amount to be withdrawn in integer values greater than 100 and less than 10,000 in multiples of 100. Output: The requested cash and printed transaction statement. Processing: the amount is debited from the user’s account if sufficient balance is available, otherwise an error message displayed Properties of a good SRS document The important properties of a good SRS document are the following: • Concise. The SRS document should be concise and at the same time unambiguous, consistent, and complete. Verbose and irrelevant descriptions reduce readability and also increase error possibilities. • Structured. It should be well-structured. A well-structured document is easy to understand and modify. In practice, the SRS document undergoes several revisions to cope up with the customer requirements. Often, the customer requirements evolve over a period of time. Therefore, in order to make the modifications to the SRS document easy, it is important to make the document well-structured. • Black-box view. It should only specify what the system should do and refrain from stating how to do these. This means that the SRS document should specify the external behavior of the system and not discuss the implementation issues. The SRS document should view the system to be developed as black box, and should specify the externally 24 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) visible behavior of the system. For this reason, the SRS document is also called the black-box specification of a system. • Conceptual integrity. It should show conceptual integrity so that the reader can easily understand it. • Response to undesired events. It should characterize acceptable responses to undesired events. These are called system response to exceptional conditions. • Verifiable. All requirements of the system as documented in the SRS document should be verifiable. This means that it should be possible to determine whether or not requirements have been met in an implementation. Problems without a SRS document The important problems that an organization would face if it does not develop a SRS document are as follows: • Without developing the SRS document, the system would not be implemented according to customer needs. • Software developers would not know whether what they are developing is what exactly required by the customer. • Without SRS document, it will be very much difficult for the maintenance engineers to understand the functionality of the system. • It will be very much difficult for user document writers to write the users’ manuals properly without understanding the SRS document. Problems with an unstructured specification • It would be very much difficult to understand that document. • It would be very much difficult to modify that document. • Conceptual integrity in that document would not be shown. • The SRS document might be unambiguous and inconsistent. DECISION TREE 25 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) A decision tree gives a graphic view of the processing logic involved in decision making and the corresponding actions taken. The edges of a decision tree represent conditions and the leaf nodes represent the actions to be performed depending on the outcome of testing the condition. Example: - Consider Library Membership Automation Software (LMS) where it should support the following three options: • New member • Renewal • Cancel membership New member optionDecision: When the 'new member' option is selected, the software asks details about the member like the member's name, address, phone number etc. Action: If proper information is entered then a membership record for the member is created and a bill is printed for the annual membership charge plus the security deposit payable. Renewal optionDecision: If the 'renewal' option is chosen, the LMS asks for the member's name and his membership number to check whether he is a valid member or not. Action: If the membership is valid then membership expiry date is updated and the annual membership bill is printed, otherwise an error message is displayed. Cancel membership optionDecision: If the 'cancel membership' option is selected, then the software asks for member's name and his membership number. Action: The membership is cancelled, a cheque for the balance amount due to the member is printed and finally the membership record is deleted from the database. The following tree (fig. 6.1) shows the graphical representation of the above example. 26 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) Fig 6.1: Decision Tree of LMS DECISION TABLE A decision table is used to represent the complex processing logic in a tabular or a matrix form. The upper rows of the table specify the variables or conditions to be evaluated. The lower rows of the table specify the actions to be taken when the corresponding conditions are satisfied. A column in a table is called a rule. A rule implies that if a condition is true, then the corresponding action is to be executed. Example: - 27 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) Consider the previously discussed LMS example. The following decision table (fig. 6.2) shows how to represent the LMS problem in a tabular form. Here the table is divided into two parts, the upper part shows the conditions and the lower part shows what actions are taken. Each column of the table is a rule. Fig. 6.2: Decision table for LMS From the above table you can easily understand that, if the valid selection condition is false then the action taken for this condition is 'display error message'. Similarly, the actions taken for other conditions can be inferred from the table. FORMAL SYSTEM SPECIFICATION 28 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) Formal Technique A formal technique is a mathematical method to specify a hardware and/or software system, verify whether a specification is realizable, verify that an implementation satisfies its specification, prove properties of a system without necessarily running the system, etc. The mathematical basis of a formal method is provided by the specification language. Formal Specification Language A formal specification language consists of two sets syn and sem, and a relation sat between them. The set syn is called the syntactic domain, the set sem is called the semantic domain, and the relation sat is called the satisfaction relation. For a given specification syn, and model of the system sem, if sat (syn, sem), then syn is said to be the specification of sem, and sem is said to be the specificand of syn. Syntactic Domains The syntactic domain of a formal specification language consists of an alphabet of symbols and set of formation rules to construct well-formed formulas from the alphabet. The well-formed formulas are used to specify a system. Semantic Domains Formal techniques can have considerably different semantic domains. Abstract data type specification languages are used to specify algebras, theories, and programs. Programming languages are used to specify functions from input to output values. Concurrent and distributed system specification languages are used to specify state sequences, event sequences, state- transition sequences, synchronization trees, partial orders, state machines, etc. Satisfaction Relation Given the model of a system, it is important to determine whether an element of the semantic domain satisfies the specifications. This satisfaction is determined by using a homomorphism known as semantic abstraction function. The semantic abstraction function maps the elements of the semantic domain into equivalent classes. There can be different specifications describing different aspects of a system model, possibly using 29 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) different specification languages. Some of these specifications describe the system’s behavior and the others describe the system’s structure. Consequently, two broad classes of semantic abstraction functions are defined: those that preserve a system’s behavior and those that preserve a system’s structure. Model-oriented vs. property-oriented approaches Formal methods are usually classified into two broad categories – model – oriented and property – oriented approaches. In a model-oriented style, one defines a system’s behavior directly by constructing a model of the system in terms of mathematical structures such as tuples, relations, functions, sets, sequences, etc. In the property-oriented style, the system's behavior is defined indirectly by stating its properties, usually in the form of a set of axioms that the system must satisfy. Example:Let us consider a simple producer/consumer example. In a property-oriented style, it is probably started by listing the properties of the system like: the consumer can start consuming only after the producer has produced an item; the producer starts to produce an item only after the consumer has consumed the last item, etc. A good example of a producer-consumer problem is CPU-Printer coordination. After processing of data, CPU outputs characters to the buffer for printing. Printer, on the other hand, reads characters from the buffer and prints them. The CPU is constrained by the capacity of the buffer, whereas the printer is constrained by an empty buffer. Examples of property-oriented specification styles are axiomatic specification and algebraic specification. In a model-oriented approach, we start by defining the basic operations, p (produce) and c (consume). Then we can state that S1 + p → S, S + c → S1. Thus the modeloriented approaches essentially specify a program by writing another, presumably simpler program. Examples of popular model-oriented specification techniques are Z, CSP, CCS, etc. Model-oriented approaches are more suited to use in later phases of life cycle because 30 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) here even minor changes to a specification may lead to drastic changes to the entire specification. They do not support logical conjunctions (AND) and disjunctions (OR). Property-oriented approaches are suitable for requirements specification because they can be easily changed. They specify a system as a conjunction of axioms and you can easily replace one axiom with another one. Operational Semantics Informally, the operational semantics of a formal method is the way computations are represented. There are different types of operational semantics according to what is meant by a single run of the system and how the runs are grouped together to describe the behavior of the system. Some commonly used operational semantics are as follows: Linear Semantics:In this approach, a run of a system is described by a sequence (possibly infinite) of events or states. The concurrent activities of the system are represented by non-deterministic interleavings of the automatic actions. For example, a concurrent activity a║b is represented by the set of sequential activities a;b and b;a. This is simple but rather unnatural representation of concurrency. The behavior of a system in this model consists of the set of all its runs. To make this model realistic, usually justice and fairness restrictions are imposed on computations to exclude the unwanted interleavings. Branching Semantics:In this approach, the behavior of a system is represented by a directed graph. The nodes of the graph represent the possible states in the evolution of a system. The descendants of each node of the graph represent the states which can be generated by any of the atomic actions enabled at that state. Although this semantic model distinguishes the branching points in a computation, still it represents concurrency by interleaving. Maximally parallel semantics:In this approach, all the concurrent actions enabled at any state are assumed to be taken together. This is again not a natural model of concurrency since it implicitly assumes the availability of all the required computational resources. 31 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) Partial order semantics:Under this view, the semantics ascribed to a system is a structure of states satisfying a partial order relation among the states (events). The partial order represents a precedence ordering among events, and constraints some events to occur only after some other events have occurred; while the occurrence of other events (called concurrent events) is considered to be incomparable. This fact identifies concurrency as a phenomenon not translatable to any interleaved representation. Formal methods possess several positive features, some of which are discussed below. • Formal specifications encourage rigor. Often, the very process of construction of a rigorous specification is more important than the formal specification itself. The construction of a rigorous specification clarifies several aspects of system behavior that are not obvious in an informal specification. • Formal methods usually have a well-founded mathematical basis. Thus, formal specifications are not only more precise, but also mathematically sound and can be used to reason about the properties of a specification and to rigorously prove that an implementation satisfies its specifications. • Formal methods have well-defined semantics. Therefore, ambiguity in specifications is automatically avoided when one formally specifies a system. • The mathematical basis of the formal methods facilitates automating the analysis of specifications. For example, a tableau-based technique has been used to automatically check the consistency of specifications. Also, automatic theorem proving techniques can be used to verify that an implementation satisfies its specifications. The possibility of automatic verification is one of the most important advantages of formal methods. • Formal specifications can be executed to obtain immediate feedback on the features of the specified system. This concept of executable specifications is related to rapid prototyping. Informally, a prototype is a “toy” working model of a system that can provide immediate feedback on the behavior of the specified system, and is especially useful in checking the completeness of specifications. 32 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) Limitations of formal requirements specification It is clear that formal methods provide mathematically sound frameworks within large, complex systems can be specified, developed and verified in a systematic rather than in an ad hoc manner. However, formal methods suffer from several shortcomings, some of which are the following: • Formal methods are difficult to learn and use. • The basic incompleteness results of first-order logic suggest that it is impossible to check absolute correctness of systems using theorem proving techniques. • Formal techniques are not able to handle complex problems. This shortcoming results from the fact that, even moderately complicated problems blow up the complexity of formal specification and their analysis. Also, a large unstructured set of mathematical formulas is difficult to comprehend. Axiomatic Specification In axiomatic specification of a system, first-order logic is used to write the pre and postconditions to specify the operations of the system in the form of axioms. The preconditions basically capture the conditions that must be satisfied before an operation can successfully be invoked. In essence, the pre-conditions capture the requirements on the input parameters of a function. The post-conditions are the conditions that must be satisfied when a function completes execution for the function to be considered to have executed successfully. Thus, the post- conditions are essentially constraints on the results produced for the function execution to be considered successful. The following are the sequence of steps that can be followed to systematically develop the axiomatic specifications of a function: • Establish the range of input values over which the function should behave correctly. Also find out other constraints on the input parameters and write it in the form of a predicate. • Specify a predicate defining the conditions which must hold on the output of the function if it behaved properly. 33 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) • Establish the changes made to the function’s input parameters after execution of the function. Pure mathematical functions do not change their input and therefore this type of assertion is not necessary for pure functions. • Combine all of the above into pre and post conditions of the function. Example1: - Specify the pre- and post-conditions of a function that takes a real number as argument and returns half the input value if the input is less than or equal to 100, or else returns double the value. f (x : real) : real pre : x ∈ R post : {(x≤100)  (f(x) = x/2)} ∨ {(x>100)  (f(x) = 2∗x)} Example2: - Axiomatically specify a function named search which takes an integer array and an integer key value as its arguments and returns the index in the array where the key value is present. search(X : IntArray, key : Integer) : Integer pre : ∃ i ∈ [Xfirst….Xlast], X[i] = key post : {(X′[search(X, key)] = key)  (X = X′)} Here the convention followed is: If a function changes any of its input parameters and if that parameter is named X, and then it is referred to as X′ after the function completes execution faster. SOFTWARE DESIGN Software design is a process to transform user requirements into some suitable form, which helps the programmer in software coding and implementation. For assessing user requirements, an SRS (Software Requirement Specification) document is created whereas for coding and implementation, there is a need of more specific and detailed requirements in software terms. The output of this process can directly be used into implementation in programming languages. 34 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) Software design is the first step in SDLC (Software Design Life Cycle), which moves the concentration from problem domain to solution domain. It tries to specify how to fulfill the requirements mentioned in SRS. Software Design Levels Software design yields three levels of results: • Architectural Design - The architectural design is the highest abstract version of the system. It identifies the software as a system with many components interacting with each other. At this level, the designers get the idea of proposed solution domain. • High-level Design- The high-level design breaks the ‘single entity-multiple component’ concept of architectural design into less-abstracted view of sub-systems and modules and depicts their interaction with each other. High-level design focuses on how the system along with all of its components can be implemented in forms of modules. It recognizes modular structure of each sub-system and their relation and interaction among each other. • Detailed Design- Detailed design deals with the implementation part of what is seen as a system and its sub-systems in the previous two designs. It is more detailed towards modules and their implementations. It defines logical structure of each module and their interfaces to communicate with other modules. Modularization Modularization is a technique to divide a software system into multiple discrete and independent modules, which are expected to be capable of carrying out task(s) independently. These modules may work as basic constructs for the entire software. Designers tend to design modules such that they can be executed and/or compiled separately and independently. Modular design unintentionally follows the rules of ‘divide and conquer’ problemsolving strategy this is because there are many other benefits attached with the modular design of a software. Advantage of modularization: 35 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) • Smaller components are easier to maintain • Program can be divided based on functional aspects • Desired level of abstraction ca n be brought in the program • Components with high cohesion can be re-used again. • Concurrent execution can be made possible • Desired from security aspect Concurrency Back in time, all softwares were meant to be executed sequentially. By sequential execution we mean that the coded instruction will be executed one after another implying only one portion of program being activated at any given time. Say, a software has multiple modules, then only one of all the modules can be found active at any time of execution. In software design, concurrency is implemented by splitting the software into multiple independent units of execution, like modules and executing them in parallel. In other words, concurrency provides capability to the software to execute more than one part of code in parallel to each other. It is necessary for the programmers and designers to recognize those modules, which can be made parallel execution. Example The spell check feature in word processor is a module of software, which runs alongside the word processor itself. Coupling and Cohesion When a software program is modularized, its tasks are divided into several modules based on some characteristics. As we know, modules are set of instructions put together in order to achieve some tasks. They are though, considered as single entity but may refer to each other to work together. There are measures by which the quality of a design of modules and their interaction among them can be measured. These measures are called coupling and cohesion. 36 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) Cohesion Cohesion is a measure that defines the degree of intra-dependability within elements of a module. The greater the cohesion, the better is the program design. There are seven types of cohesion, namely – • Co-incidental cohesion - It is unplanned and random cohesion, which might be the result of breaking the program into smaller modules for the sake of modularization. Because it is unplanned, it may serve confusion to the programmers and is generally not-accepted. • Logical cohesion - When logically categorized elements are put together into a module, it is called logical cohesion. • Temporal Cohesion - When elements of module are organized such that they are processed at a similar point in time, it is called temporal cohesion. • Procedural cohesion - When elements of module are grouped together, which are executed sequentially in order to perform a task, it is called procedural cohesion. • Communicational cohesion - When elements of module are grouped together, which are executed sequentially and work on same data (information), it is called communicational cohesion. • Sequential cohesion - When elements of module are grouped because the output of one element serves as input to another and so on, it is called sequential cohesion. • Functional cohesion - It is considered to be the highest degree of cohesion, and it is highly expected. Elements of module in functional cohesion are grouped because they all contribute to a single well-defined function. It can also be reused. Coupling Coupling is a measure that defines the level of inter-dependability among modules of a program. It tells at what level the modules interfere and interact with each other. The lower the coupling, the better the program. There are five levels of coupling, namely - • Content coupling - When a module can directly access or modify or refer to the content 37 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) of another module, it is called content level coupling. • Common coupling- When multiple modules have read and write access to some global data, it is called common or global coupling. • Control coupling- Two modules are called control-coupled if one of them decides the function of the other module or changes its flow of execution. • Stamp coupling- When multiple modules share common data structure and work on different part of it, it is called stamp coupling. • Data coupling- Data coupling is when two modules interact with each other by means of passing data (as parameter). If a module passes data structure as parameter, then the receiving module should use all its components. Ideally, no coupling is considered to be the best. Design Verification The output of software design process is design documentation, pseudo codes, detailed logic diagrams, process diagrams, and detailed description of all functional or nonfunctional requirements. The next phase, which is the implementation of software, depends on all outputs mentioned above. It is then becomes necessary to verify the output before proceeding to the next phase. The early any mistake is detected, the better it is or it might not be detected until testing of the product. If the outputs of design phase are in formal notation form, then their associated tools for verification should be used otherwise a thorough design review can be used for verification and validation.By structured verification approach, reviewers can detect defects that might be caused by overlooking some conditions. A good design review is important for good software design, accuracy and quality. SOFTWARE DESIGN STRATEGIES Software design is a process to conceptualize the software requirements into software implementation. Software design takes the user requirements as challenges and tries to find optimum solution. While the software is being conceptualized, a plan is chalked out 38 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) to find the best possible design for implementing the intended solution. Software design is a process to conceptualize the software requirements into software implementation. Software design takes the user requirements as challenges and tries to find optimum solution. While the software is being conceptualized, a plan is chalked out to find the best possible design for implementing the intended solution. There are multiple variants of software design. Let us study them briefly: Structured Design Structured design is a conceptualization of problem into several well-organized elements of solution. It is basically concerned with the solution design. Benefit of structured design is, it gives better understanding of how the problem is being solved. Structured design also makes it simpler for designer to concentrate on the problem more accurately. Structured design is mostly based on ‘divide and conquer’ strategy where a problem is broken into several small problems and each small problem is individually solved until the whole problem is solved. The small pieces of problem are solved by means of solution modules. Structured design emphasis that these modules be well organized in order to achieve precise solution. These modules are arranged in hierarchy. They communicate with each other. A good structured design always follows some rules for communication among multiple modules, namely - Cohesion - grouping of all functionally related elements. Coupling - communication between different modules. A good structured design has high cohesion and low coupling arrangements. Function Oriented Design In function-oriented design, the system is comprised of many smaller sub-systems known as functions. These functions are capable of performing significant task in the system. 39 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) The system is considered as top view of all functions. Function oriented design inherits some properties of structured design where divide and conquer methodology is used. This design mechanism divides the whole system into smaller functions, which provides means of abstraction by concealing the information and their operation. These functional modules can share information among themselves by means of information passing and using information available globally. Another characteristic of functions is that when a program calls a function, the function changes the state of the program, which sometimes is not acceptable by other modules. Function oriented design works well where the system state does not matter and program/functions work on input rather than on a state. Design Process • The whole system is seen as how data flows in the system by means of data flow diagram. • DFD depicts how functions change the data and state of entire system. • The entire system is logically broken down into smaller units known as functions on the basis of their operation in the system. • Each function is then described at large. Object Oriented Design Object oriented design works around the entities and their characteristics instead of functions involved in the software system. This design strategy focuses on entities and its characteristics. The whole concept of software solution revolves around the engaged entities. Let us see the important concepts of Object Oriented Design: 40 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) • Objects - All entities involved in the solution design are known as objects. For example, person, banks, company and customers are treated as objects. Every entity has some attributes associated to it and has some methods to perform on the attributes. • Classes - A class is a generalized description of an object. An object is an instance of a class. Class defines all the attributes, which an object can have and methods, which defines the functionality of the object. In the solution design, attributes are stored as variables and functionalities are defined by means of methods or procedures. • Encapsulation - In OOD, the attributes (data variables) and methods (operation on the data) are bundled together is called encapsulation. Encapsulation not only bundles important information of an object together, but also restricts access of the data and methods from the outside world. This is called information hiding. • Inheritance - OOD allows similar classes to stack up in hierarchical manner where the lower or sub-classes can import, implement and re-use allowed variables and methods from their immediate super classes. This property of OOD is known as inheritance. This makes it easier to define specific class and to create generalized classes from specific ones. • Polymorphism - OOD languages provide a mechanism where methods performing similar tasks but vary in arguments, can be assigned same name. This is called polymorphism, which allows a single interface performing tasks for different types. Depending upon how the function is invoked, respective portion of the code gets executed. Design Process Software design process can be perceived as series of well-defined steps. Though it varies according to design approach (function oriented or object oriented, yet It may have the following steps involved: • A solution design is created from requirement or previous used system and/or system sequence diagram. 41 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) • Objects are identified and grouped into classes on behalf of similarity in attribute characteristics. • Class hierarchy and relation among them are defined. • Application framework is defined. Software Design Approaches There are two generic approaches for software designing: Top down Design We know that a system is composed of more than one sub-systems and it contains a number of components. Further, these sub-systems and components may have their one set of sub-system and components and creates hierarchical structure in the system. Top-down design takes the whole software system as one entity and then decomposes it to achieve more than one sub-system or component based on some characteristics. Each sub-system or component is then treated as a system and decomposed further. This process keeps on running until the lowest level of system in the top-down hierarchy is achieved. Top-down design starts with a generalized model of system and keeps on defining the more specific part of it. When all components are composed the whole system comes into existence. Top-down design is more suitable when the software solution needs to be designed from scratch and specific details are unknown. Bottom-up Design The bottom up design model starts with most specific and basic components. It proceeds with composing higher level of components by using basic or lower level components. It keeps creating higher level components until the desired system is not evolved as one single component. With each higher level, the amount of abstraction is increased. Bottom-up strategy is more suitable when a system needs to be created from some 42 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) existing system, where the basic primitives can be used in the newer system. Both, top-down and bottom-up approaches are not practical individually. Instead, a good combination of both is used. SOFTWARE ANALYSIS & DESIGN TOOLS Software analysis and design includes all activities, which help the transformation of requirement specification into implementation. Requirement specifications specify all functional and non-functional expectations from the software. These requirement specifications come in the shape of human readable and understandable documents, to which a computer has nothing to do. Software analysis and design is the intermediate stage, which helps human-readable requirements to be transformed into actual code. Let us see few analysis and design tools used by software designers: Data Flow Diagram Data flow diagram is a graphical representation of data flow in an information system. It is capable of depicting incoming data flow, outgoing data flow and stored data. The DFD does not mention anything about how data flows through the system. There is a prominent difference between DFD and Flowchart. The flowchart depicts flow of control in program modules. DFDs depict flow of data in the system at various levels. DFD does not contain any control or branch elements. Types of DFD Data Flow Diagrams are either Logical or Physical. • Logical DFD - This type of DFD concentrates on the system process and flow of data in the system. For example in a Banking software system, how data is moved between different entities. 43 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) • Physical DFD - This type of DFD shows how the data flow is actually implemented in the system. It is more specific and close to the implementation. DFD Components DFD can represent Source, destination, storage and flow of data using the following set of components – Fig 10.1: DFD Components • Entities - Entities are source and destination of information data. Entities are represented by rectangles with their respective names. • Process - Activities and action taken on the data are represented by Circle or Roundedged rectangles. • Data Storage - There are two variants of data storage - it can either be represented as a rectangle with absence of both smaller sides or as an open-sided rectangle with only one side missing. • Data Flow - Movement of data is shown by pointed arrows. Data movement is shown from the base of arrow as its source towards head of the arrow as destination. Importance of DFDs in a good software design 44 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) The main reason why the DFD technique is so popular is probably because of the fact that DFD is a very simple formalism – it is simple to understand and use. Starting with a set of high-level functions that a system performs, a DFD model hierarchically represents various sub-functions. In fact, any hierarchical model is simple to understand. Human mind is such that it can easily understand any hierarchical model of a system – because in a hierarchical model, starting with a very simple and abstract model of a system, different details of the system are slowly introduced through different hierarchies. The data flow diagramming technique also follows a very simple set of intuitive concepts and rules. DFD is an elegant modeling technique that turns out to be useful not only to represent the results of structured analysis of a software problem, but also for several other applications such as showing the flow of documents or items in an organization. Data Dictionary A data dictionary lists all data items appearing in the DFD model of a system. The data items listed include all data flows and the contents of all data stores appearing on the DFDs in the DFD model of a system. A data dictionary lists the purpose of all data items and the definition of all composite data items in terms of their component data items. For example, a data dictionary entry may represent that the data grossPay consists of the components regularPay and overtimePay. grossPay = regularPay + overtimePay For the smallest units of data items, the data dictionary lists their name and their type. Composite data items can be defined in terms of primitive data items using the following data definition operators: +: denotes composition of two data items, e.g. a+b represents data a and b. [,,]: represents selection, i.e. any one of the data items listed in the brackets can occur. For example, [a,b] represents either a occurs or b occurs. (): the contents inside the bracket represent optional data which may or may not appear. e.g. a+(b) represents either a occurs or a+b occurs. {}: represents iterative data definition, e.g. {name}5 represents five name data. {name}* represents zero or more instances of name data. 45 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) =: represents equivalence, e.g. a=b+c means that a represents b and c. /* */: Anything appearing within /* and */ is considered as a comment. Example 1: Tic-Tac-Toe Computer Game Tic-tac-toe is a computer game in which a human player and the computer make alternative moves on a 3×3 square. A move consists of marking previously unmarked square. The player who first places three consecutive marks along a straight line on the square (i.e. along a row, column, or diagonal) wins the game. As soon as either the human player or the computer wins, a message congratulating the winner should be displayed. If neither player manages to get three consecutive marks along a straight line, but all the squares on the board are filled up, then the game is drawn. The computer always tries to win a game. (a) 46

ISBN : 978-81-963532-2-3 (E-Book) Fig 10.2 (a) Level 0 (b) Level 1 DFD for Tic-Tac-Toe game It may be recalled that the DFD model of a system typically consists of several DFDs: level 0, level 1, etc. However, a single data dictionary should capture all the data appearing in all the DFDs constituting the model. Figure 10.2 represents the level 0 and level 1 DFDs for the tic-tac- toe game. The data dictionary for the model is given below. Data Dictionary for the DFD model in Example 1 move: integer /*number between 1 and 9 */ display: game+result game: board board: {integer}9 result: [“computer won”, “human won” “draw”] It may be recalled that the DFD model of a system typically consists of several DFDs: level 0, level 1, etc. However, a single data dictionary should capture all the data appearing in all the DFDs constituting the model. Figure 10.2 represents the level 0 and level 1 DFDs for the tic-tac- toe game. The data dictionary for the model is given below. Data Dictionary for the DFD model in Example 1 move: integer /*number between 1 and 9 */ display: game+result game: board board: {integer}9 result: [“computer won”, “human won” “draw”] Importance of Data Dictionary A data dictionary plays a very important role in any software development process because of the following reasons: • A data dictionary provides a standard terminology for all relevant data for use by the engineers working in a project. A consistent vocabulary for data items is very important, since in large projects different engineers of the project have a tendency to use different terms to refer to the same data, which unnecessary causes confusion. • The data dictionary provides the analyst with a means to determine the definition of different data structures in terms of their component elements. 47 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) STRUCTUREDDESIGN The aim of structured design is to transform the results of the structured analysis (i.e. a DFDrepresentation)intoastructurechart.Structureddesignprovidestwostrategiestoguidetransforma tionof a DFDinto astructurechart. • Transformanalysis • Transactionanalysis Normally, one starts with the level 1 DFD, transforms it into module representation using eitherthe transform or the transaction analysis and then proceeds towards the lower-level DFDs. Ateach level of transformation, it is important to first determine whether the transform or thetransaction analysis is applicable to a particular DFD. These are discussed in the subsequent sub-sections. StructureChart 48 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) A structure chart represents the software architecture, i.e. the various modules making up thesystem, the dependency (which module calls which other modules), and the parameters that arepassed among the different modules. Hence, the structure chart representation can be easilyimplemented using someprogramming language.Since the mainfocusina structure chartrepresentation is on the module structure of the software and the interactions among differentmodules,theproceduralaspects(e.g.howaparticularfunctionalityisachieved)arenotreprese nted. Thebasic buildingblocks which areusedto design structurecharts arethefollowing:  Rectangularboxes:Representsamodule.  Moduleinvocationarrows:Controlispassedfromononemoduletoanothermodulein thedirection ofthe connectingarrow.  Dataflowarrows:Arrowsareannotatedwithdataname;nameddatapassesfromone module to another module in thedirection ofthe arrow.  Librarymodules:Representedbyarectangle withdoubleedges.  Selection:Representedbyadiamondsymbol.  Repetition:Represented byalooparoundthecontrol flowarrow. StructureChartvs. FlowChart We are all familiar with the flow chart representation of a program. Flow chart is a convenienttechnique to represent the flow of control in a program. A structurechart differs from a flowchartin threeprincipal ways: • It is usually difficult to identify the different modules of the software from its flow chartrepresentation. • Datainterchange amongdifferent modulesisnotrepresentedinaflowchart. • Sequentialorderingoftasksinherentinaflowchartissuppressedinastructurechart. TransformAnalysis Transform analysis identifies the primary functional components (modules) and the high levelinputs and outputs for these components. The first step in transform analysis is to divide the DFDinto3 types of parts: • Input • Logicalprocessing • Output 49 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) The input portion of the DFD includes processes that transform input data from physical (e.g.character from terminal) to logical forms (e.g. internal tables, lists, etc.). Each input portion iscalledanafferent branch. The output portion of a DFD transforms output data from logical to physical form. Each outputportion is calledanefferent branch.The remaining portion of a DFD is calledthe centraltransform. In the next step of transform analysis, the structure chart is derived by drawing one functionalcomponentforthecentraltransform, and theafferent and efferentbranches. These are drawn below a root module, which would invoke these modules.Identifying thehighest level input and output transforms requires experience and skill. One possible approach isto trace the inputs until a bubble is found whose output cannot be deduced from its inputs alone.Processes which validate input or add information to them are not central transforms. Processeswhichsortinputorfilterdatafromitare.Thefirstlevelstructurechartisproducedbyrepresenti ngeachinputandoutputunitasboxesandeachcentraltransformasasinglebox.Inthethirdstepoftransfor manalysis,thestructurechartisrefinedbyaddingsub-functionsrequired by each of the high-level functional components. Many levels of functional componentsmay be added. This process of breaking functional components into subcomponents is calledfactoring.Factoringincludesaddingreadandwritemodules,errorhandlingmodules,initializationandterminationprocess,identifyingcustomermodules,etc.Thefactor ingprocessiscontinued until all bubblesin theDFDarerepresented in the structurechart. 50 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) Example:StructurechartfortheRMS software Forthisexample,thecontextdiagramwasdrawnearlier. Todrawthelevel1DFD(fig.11.1),fromacursoryanalysisoftheproblemdescription, we can see that there are four basic functions that the system needs toperform – accept the input numbers from the user, validate the numbers, calculate therootmean squareof theinput numbers and, then displaythe result. Fig.11.1:Level1DFD By observing the level 1 DFD, we identify the validate-input as the afferent branch and writeoutputastheefferentbranch.Theremainingportion(i.e.compute-rms)formsthecentraltransform. By applying the step 2 and step 3 of transform analysis, we get the structure chartshownin fig.11.2. 51 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) 52 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) Fig.11.2:StructureChart TransactionAnalysis A transaction allows the user to perform some meaningful piece of work. Transaction analysisis useful while designing transaction processing programs. In a transaction-driven system, oneof several possible paths through the DFD is traversed depending upon the input data item.This is in contrast to a transform centered system which is characterized by similar processingsteps for each data item. Each different way in which input data is handled is a transaction. Asimple way to identify a transaction is to check the input data. The number of bubbles onwhich the input data to the DFD are incident defines the number of transactions. However,some transaction may not require any input data. These transactions can be identified from theexperienceof solvingalargenumber ofexamples. For each identified transaction, trace the input data to the output. All the traversed bubblesbelongtothetransaction.Thesebubblesshouldbemappedtothesamemoduleonthestructure 53 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) chart. In the structure chart, draw a root module and below this module draw eachidentifiedtransactionamodule.Everytransactioncarriesatag,whichidentifiesitstype. 54 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) Transactionanalysisusesthistagtodividethesystemintotransactionmodulesandatransactioncentermodule. Thestructurechartforthesupermarketprizeschemesoftwareis showninfig.11.3. Fig.11.3:StructureChartforthesupermarketprizescheme 55 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) MODULE2 OBJECTMODELLINGUSINGUML Model A model captures aspects important for some application while omitting (or abstracting) the rest.A model in the context of software development can be graphical, textual, mathematical, orprogram code-based. Models are very useful in documenting the design and analysis results.Models also facilitate the analysis and design procedures themselves. Graphical models are verypopularbecausetheyareeasytounderstandandconstruct.UMLisprimarilyagraphicalmodelingto ol. However, it oftenrequires textexplanations toaccompanythegraphical models. Needforamodel An important reason behind constructing a model is that it helps manage complexity. Oncemodels of a system have been constructed, these can be used for a variety of purposes duringsoftwaredevelopment, includingthe following: • Analysis • Specification • Codegeneration • Design • Visualizeandunderstandtheproblemandtheworkingof asystem • Testing,etc. Inalltheseapplications,theUMLmodelscannotonlybeusedtodocumenttheresultsbutalsoto arrive at the results themselves. Since a model can be used for a variety of purposes, it isreasonable to expect that the model would vary depending on the purpose for which it is beingconstructed. For example, a model developed for initial analysis and specification should be verydifferent from the one used for design. A model that is being used for analysis and specificationwould not show any of the design decisions that would be made later on during the design stage.On the other hand, a model used for design purposes should capture all the design decisions.Therefore, it is a 56 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) good idea to explicitly mention the purpose for which a model has beendeveloped, alongwith themodel. Unified Modeling Language (UML) UML, as the name implies, is a modeling language. It may be used to visualize, specify, construct, and document the artifacts of a software system. It provides a set of notations (e.g. rectangles, lines, ellipses, etc.) to create a visual model of the system. Like any other language, UML has its own syntax (symbols and sentence formation rules) and semantics (meanings of symbols and sentences). Also, we should clearly understand that UML is not a system design or development methodology, but can be used to document objectoriented and analysis results obtained using some methodology. Origin of UML In the late 1980s and early 1990s, there was a proliferation of object-oriented design techniques and notations. Different software development houses were using different notations to document their object-oriented designs. These diverse notations used to give rise to a lot of confusion. UML was developed to standardize the large number of object-oriented modeling notations that existed and were used extensively in the early 1990s. The principles ones in use were: • Object Management Technology [Rumbaugh 1991] • Booch’s methodology [Booch 1991] • Object-Oriented Software Engineering [Jacobson 1992] • Odell’s methodology [Odell 1992] • Shaler and Mellor methodology [Shaler 1992] It is needless to say that UML has borrowed many concepts from these modeling techniques. Especially, concepts from the first three methodologies have been heavily 57 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) drawn upon. UML was adopted by Object Management Group (OMG) as a de facto standard in 1997. OMG is an association of industries which tries to facilitate early formation of standards. We shall see that UML contains an extensive set of notations and suggests construction of many types of diagrams. It has successfully been used to model both large and small problems. The elegance of UML, its adoption by OMG, and a strong industry backing have helped UML find widespread acceptance. UML is now being used in a large number of software development projects worldwide. UML Diagrams UML can be used to construct nine different types of diagrams to capture five different views of a system. Just as a building can be modeled from several views (or perspectives) such as ventilation perspective, electrical perspective, lighting perspective, heating perspective, etc.; the different UML diagrams provide different perspectives of the software system to be developed and facilitate a comprehensive understanding of the system. Such models can be refined to get the actual implementation of the system. The UML diagrams can capture the following five views of a system: • User’s view • Structural view • Behavioral view • Implementation view • Environmental view 58 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) Fig.12.1:Different typesofdiagrams andviewssupported inUML User’s view: This view defines the functionalities (facilities) made available by the system to its users. The users’ view captures the external users’ view of the system in terms of the functionalities offered by the system. The users’ view is a black-box view of the system where the internal structure, the dynamic behavior of different system components, the implementation etc. are not visible. The users’ view is very different from all other views in the sense that it is a functional model compared to the object model of all other views. The users’ view can be considered as the central view and all other views are expected to conform to this view. This thinking is in fact the crux of any user centric development style. Structural view: The structural view defines the kinds of objects (classes) important to the understanding of the working of a system and to its implementation. It also captures the relationships among the classes (objects). The structural model is also called the static model, since the structure of a system does not change with time. Behavioral view: The behavioral view captures how objects interact with each other to realize the system behavior. The system behavior captures the time-dependent (dynamic) behavior of the system. Implementation view: This view captures the important components of the system and their dependencies. Environmental view: This view models how the different components are implemented on different pieces of hardware. USECASE DIAGRAM UseCaseModel The use case model for any system consists of a set of “use cases”. Intuitively, use 59 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) casesrepresent the different ways in which a system can be used by the users. A simple way to find allthe use cases of a system is to ask the question: “What the users can do using the system?” ThusfortheLibraryInformation System (LIS), theusecasescould be: • issue-book • query-book • return-book • create-member • add-book,etc Use cases correspond to the high-level functional requirements. The use cases partition thesystem behavior into transactions, such that each transaction performs some useful action fromthe user’s point of view. To complete each transaction may involve either a single message ormultiplemessageexchanges betweentheuser andthe system tocomplete. Purposeofusecases The purpose of a use case is to define a piece of coherent behavior without revealing the internalstructure of the system. The use cases do not mention any specific algorithm to be used or theinternaldatarepresentation,internalstructureofthesoftware,etc.Ausecasetypicallyrepresents a sequence of interactions between the user and the system. These interactions consistof one mainline sequence. The mainline sequence represents the normal interaction between auser and the system. The mainline sequence is the most occurring sequence of interaction. Forexample, the mainline sequence of the withdraw cash use case supported by a bank ATM drawn,complete the transaction, and get the amount. Several variations to the main line sequence mayalsoexist.Typically,avariationfromthemainlinesequenceoccurswhensomespecificconditions hold. For the bank ATM example, variations or alternate scenarios may occur, if thepassword is invalid or the amount to be withdrawn exceeds the amount balance. The variationsare also called alternative paths. A use case can be viewed as a set of related scenarios tiedtogetherbyacommongoal. Themainlinesequenceandeachofthevariationsarecalledscenarios or instances of the use case. Each scenario is a single path of user events and systemactivitythroughthe usecase. Representation of Use Cases Use cases can be represented by drawing a use case diagram and writing an accompanying text 60 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) elaborating the drawing. In the use case diagram, each use case is represented by an ellipse with the name of the use case written inside the ellipse. All the ellipses (i.e. use cases) of a system are enclosed within a rectangle which represents the system boundary. The name of the system being modeled (such as Library Information System) appears inside the rectangle. The different users of the system are represented by using the stick person icon. Each stick person icon is normally referred to as an actor. An actor is a role played by a user with respect to the system use. It is possible that the same user may play the role of multiple actors. Each actor can participate in one or more use cases. The line connecting the actor and the use case is called the communication relationship. It indicates that the actor makes use of the functionality provided by the use case. Both the human users and the external systems can be represented by stick person icons. When a stick person icon represents an external system, it is annotated by the stereotype <<external system>>. Example 1: Tic-Tac-Toe Computer Game Tic-tac-toe is a computer game in which a human player and the computer make alternative moves on a 3×3 square. A move consists of marking previously unmarked square. The player who first places three consecutive marks along a straight line on the square (i.e. along a row, column, or diagonal) wins the game. As soon as either the human player or the computer wins, a message congratulating the winner should be displayed. If neither player manages to get three consecutive marks along a straight line, but all the squares on the board are filled up, then the game is drawn. The computer always tries to win a game. The use case model for the Tic-tac-toe problem is shown in fig. 13.1. This software has only one use case “play move”. Note that the use case “get-user- move” is not used here. The name “get-user-move” would be inappropriate because the use cases should be named from the user’s perspective. Fig.13.1:Usecasemodelfortic-tac-toegame Text Description Each ellipse on the use case diagram should be accompanied by a text description. The text description 61 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) should define the details of the interaction between the user and the computer and other aspects of the use case. It should include all the behavior associated with the use case in terms of the mainline sequence, different variations to the normal behavior, the system responses associated with the use case, the exceptional conditions that may occur in the behavior, etc. The behavior description is often written in a conversational style describing the interactions between the actor and the system. The text description may be informal, but some structuring is recommended. The following are some of the information which may be included in a use case text description in addition to the mainline sequence, and the alternative scenarios. Contact persons: This section lists the personnel of the client organization with whom the use case was discussed, date and time of the meeting, etc. Actors: In addition to identifying the actors, some information about actors using this use case which may help the implementation of the use case may be recorded. Pre-condition: The preconditions would describe the state of the system before the use case execution starts. Post-condition: This captures the state of the system after the use case has successfully completed. Non-functional requirements: This could contain the important constraints for the design and implementation, such as platform and environment conditions, qualitative statements, response time requirements, etc. Exceptions, error situations: This contains only the domain-related errors such as lack of user’s access rights, invalid entry in the input fields, etc. Obviously, errors that are not domain related, such as software errors, need not be discussed here. Sample dialogs: These serve as examples illustrating the use case. Specific user interface requirements: These contain specific requirements for the user interface of the use case. For example, it may contain forms to be used, screen shots, interaction style, etc. Document references: This part contains references to specific domain-related documents which may be useful to understand the system operation Example 2: A supermarket needs to develop the following software to encourage regular customers. For this, the customer needs to supply his/her residence address, telephone number, and the driving license number. Each customer who registers for this scheme is assigned a unique customer number (CN) by the computer. A customer can present his CN to the checkout staff when he makes any purchase. In this case, the value of his purchase is credited against his CN. At the end of each year, the supermarket intends to award surprise gifts to 10 customers who make the highest total purchase over the year. Also, it intends to award a 22 caret gold coin to every customer whose purchase exceeded Rs.10,000. The entries against the CN are the reset on the day of every year after the prize winners’ lists are generated. 62 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) The use case model for the Supermarket Prize Scheme is shown in fig. 13.2. As discussed earlier, the use cases correspond to the high-level functional requirements. From the problem description, we can identify three use cases: “register-customer”, “register-sales”, and “select- winners”. As a sample, the text description for the use case “register-customer” is shown. Fig.13.2UsecasemodelforSupermarketPrizeScheme Text description U1: register-customer: Using this use case, the customer can register himself by providing the necessary details. Scenario 1: Mainline sequence 1. Customer: select register customer option. 2. System: display prompt to enter name, address, and telephone number. 3. Customer: enter the necessary values. 4. System: display the generated id and the message that the customer has been successfully registered. 63 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) Scenario 2: at step 4 of mainline sequence 1. System: displays the message that the customer has already registered. Scenario 2: at step 4 of mainline sequence 1. System: displays the message that some input information has not been entered. The system displays a prompt to enter the missing value. The description for other use cases is written in a similar fashion. Utility of use case diagrams From use case diagram, it is obvious that the utility of the use cases are represented by ellipses. They along with the accompanying text description serve as a type of requirements specification of the system and form the core model to which all other models must conform. But, what about the actors (stick person icons)? One possible use of identifying the different types of users (actors) is in identifying and implementing a security mechanism through a login system, so that each actor can involve only those functionalities to which he is entitled to. Another possible use is in preparing the documentation (e.g. users’ manual) targeted at each category of user. Further, actors help in identifying the use cases and understanding the exact functioning of the system. Factoring of use cases It is often desirable to factor use cases into component use cases. Actually, factoring of use cases are required under two situations. First, complex use cases need to be factored into simpler use cases. This would not only make the behavior associated with the use case much more comprehensible, but also make the corresponding interaction diagrams more tractable. Without decomposition, the interaction diagrams for complex use cases may become too large to be accommodated on a single sized (A4) paper. Secondly, use cases need to be factored whenever there is common behavior across different use cases. Factoring would make it possible to define such behavior only once and reuse it whenever required. It is desirable to factor out common usage such as error handling from a set of use cases. This makes analysis of the class design much simpler and elegant. However, a word of caution here. Factoring of use cases should not be done except for achieving the above two objectives. From the design point of view, it is not advantageous to break up a use case into many smaller parts just for the sake of it. 64 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) UML offers three mechanisms for factoring of use cases as follows: 1. Generalization Use case generalization can be used when one use case that is similar to another, but does something slightly differently or something more. Generalization works the same way with use cases as it does with classes. The child use case inherits the behavior and meaning of the parent use case. The notation is the same too (as shown in fig. 13.3). It is important to remember that the base and the derived use cases are separate use cases and should have separate text descriptions. Fig.13.3:Representationofusecasegeneralization 65 ISBN : 978-81-963532-2-3 (E-Book) Includes The includes relationship in the older versions of UML (prior to UML 1.1) was known as the uses relationship. The includes relationship involves one use case including the behavior of another use case in its sequence of events and actions. The includes relationship occurs when a chunk of behavior that is similar across a number of use cases. The factoring of such behavior will help in not repeating the specification and implementation across different use cases. Thus, the includes relationship explores the issue of reuse by factoring out the commonality across use cases. It can also be gainfully employed to decompose a large and complex use cases into more manageable parts. As shown in fig. 13.4 the includes relationship is represented using a predefined stereotype <<include>>.In the includes relationship, a base use case compulsorily and automatically includes the behavior of the common use cases. As shown in example fig. 13.5, issuebook and renew-book both include check-reservation use case. The base use case may include several use cases. In such cases, it may interleave their associated common use cases together. The common use case becomes a separate use case and the independent text description should be provided for it. Fig.13.4Representationofusecaseinclusion 66

ISBN : 978-81-963532-2-3 (E-Book) Fig.13.5:Exampleusecaseinclusion Extends The main idea behind the extends relationship among the use cases is that it allows you to show optional system behavior. An optional system behavior is extended only under certain conditions. This relationship among use cases is also predefined as a stereotype as shown in fig. 13.6. The extends relationship is similar to generalization. But unlike generalization, the extending use case can add additional behavior only at an extension point only when certain conditions are satisfied. The extension points are points within the use case where variation to the mainline (normal) action sequence may occur. The extends relationship is normally used to capture alternate paths or scenarios. Fig.13.6:Exampleusecaseextension Organizationofusecases When the use cases are factored, they are organized hierarchically. The high-level use cases arerefinedintoasetofsmallerandmorerefinedusecasesasshowninfig.13.7.Top-levelusecases are superordinate to the refined use cases. The refined use cases are sub-ordinate to thetoplevelusecases.Notethatonlythecomplexusecasesshouldbedecomposedandorganizedin a hierarchy. It is not necessary to decompose simple use cases. The functionality of the superordinateusecasesistraceabletotheirsub-ordinateusecases.Thus,thefunctionalityprovidedby the superordinate use cases is composite of the functionality of the sub-ordinate use cases. Inthe highest level of the use case model, only the fundamental use cases are shown. The focus ison the application context. Therefore, this level is also referred to as the context diagram. In thecontext diagram, the system limits are emphasized.In the top-level diagram, only those usecaseswithwhichexternalusersofthesystem.Thesubsystem-levelusecasesspecify theservices offered by the subsystems. Any number of levels involving the subsystems may beutilized. In the lowest level of the use case hierarchy, the class-level use cases specify thefunctionalfragments or operations offered 67 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) bytheclasses. Fig.13.7:Hierarchicalorganizationofusecases CLASSDIAGRAMS A class diagram describes the static structure of a system. It shows how a system is structured rather than how it behaves. The static structure of a system comprises of a number of class diagrams and their dependencies. The main constituents of a class diagram are classes and their relationships: generalization, aggregation, association, and various kinds of dependencies. Classes 68 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) The classes represent entities with common features, i.e. attributes and operations. Classes are represented as solid outline rectangles with compartments. Classes have a mandatory name compartment where the name is written centered in boldface. The class name is usually written using mixed case convention and begins with an uppercase. The class names are usually chosen to be singular nouns. Classes have optional attributes and operations compartments. A class may appear on several diagrams. Its attributes and operations are suppressed on all but one diagram. Attributes An attribute is a named property of a class. It represents the kind of data that an object might contain. Attributes are listed with their names, and may optionally contain specification of their type, an initial value, and constraints. The type of the attribute is written by appending a colon and the type name after the attribute name. Typically, the first letter of a class name is a small letter. An example for an attribute is given. bookName : String Operation Operation is the implementation of a service that can be requested from any object of the class to affect behaviour. An object’s data or state can be changed by invoking an operation of the object. A class may have any number of operations or no operation at all. Typically, the first letter of an operation name is a small letter. Abstract operations are written in italics. The parameters of an operation (if any), may have a kind specified, which may be ‘in’, ‘out’ or ‘inout’. An operation may have a return type consisting of a single return type expression. An example for an operation is given. issueBook(in bookName):Boolean Association Associations are needed to enable objects to communicate with each other. An association describes a connection between classes. The association relation between two objects is called object connection or link. Links are instances of associations. A link is a physical or conceptual connection between object instances. For example, suppose Amit has borrowed the book Graph Theory. Here,borrowed is the connection between the objects Amit and Graph Theory book. Mathematically, a link can be considered to be a tuple, i.e. an ordered list of object instances. An association describes a group of links with a common structure and common semantics. For example, consider the statement that Library Member borrows Books. 69 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) Here, borrows is the association between the class LibraryMember and the class Book. Usually, an association is a binary relation (between two classes). However, three or more different classes can be involved in an association. A class can have an association relationship with itself (called recursive association). In this case, it is usually assumed that two different objects of the class are linked by the association relationship. Association between two classes is represented by drawing a straight line between the concerned classes. Fig. 14.1 illustrates the graphical representation of the association relation. The name of the association is written alongside the association line. An arrowhead may be placed on the association line to indicate the reading direction of the association. The arrowhead should not be misunderstood to be indicating the direction of a pointer implementing an association. On each side of the association relation, the multiplicity is noted as an individual number or as a value range. The multiplicity indicates how many instances of one class are associated with each other. Value ranges of multiplicity are noted by specifying the minimum and maximum value, separated by two dots, e.g. 1.5. An asterisk is a wild card and means many (zero or more). The association of fig. 14.1 should be read as “Many books may be borrowed by a Library Member”. Observe that associations (and links) appear as verbs in the problem statement. Fig. Association between two classes Associations are usually realized by assigning appropriate reference attributes to the 70 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) classes involved. Thus, associations can be implemented using pointers from one object class to another. Links and associations can also be implemented by using a separate class that stores which objects of a class are linked to which objects of another class. Some CASE tools use the role names of the association relation for the corresponding automatically generated attribute. Aggregation Aggregation is a special type of association where the involved classes represent a wholepart relationship. The aggregate takes the responsibility of forwarding messages to the appropriate parts. Thus, the aggregate takes the responsibility of delegation and leadership. When an instance of one object contains instances of some other objects, then aggregation (or composition) relationship exists between the composite object and the component object. Aggregation is represented by the diamondsymbol at the composite end of a relationship. The number of instances of the component class aggregated can also be shown as in fig. 14.2 Fig:Representationofaggregation Aggregation relationship cannot be reflexive (i.e. recursive). That is, an object cannot contain objects of the same class as itself. Also, the aggregation relation is not symmetric. That is, two classes A and B cannot contain instances of each other. However, the aggregation relationship can be transitive. In this case, aggregation may consist of an arbitrary number of levels. Composition Composition is a stricter form of aggregation, in which the parts are existence-dependent on the whole. This means that the life of the parts closely ties to the life of the whole. When the whole is created, the parts are created and when the whole is destroyed, the parts are destroyed. A typical example of composition is an invoice object with invoice items. As soon as the invoice object is created, all the invoice items in it are created and as soon as the invoice object is destroyed, all invoice items in it are also destroyed. The composition relationship is represented as a filled diamond drawn at the composite-end. An example of the composition relationship is shown in fig. 14.3 71 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) Fig : Representation of composition Association vs. Aggregation vs. Composition • Association is the most general (m:n) relationship. Aggregation is a stronger relationship where one is a part of the other. Composition is even stronger than aggregation, ties the lifecycle of the part and the whole together. • Association relationship can be reflexive (objects can have relation to itself), but aggregation cannot be reflexive. Moreover, aggregation is anti-symmetric (If B is a part of A, A cannot be a part of B). • Composition has the property of exclusive aggregation i.e. an object can be a part of only one composite at a time. For example, a Frame belongs to exactly one Window,whereas in simple aggregation, a part may be shared by several objects. For example, a Wall may be a part of one or more Room objects. •  in general, the lifetime of parts and composite coincides  parts with non-fixed multiplicity may be created after composite itself  parts might be explicitly removed before the death of the composite For example, when a Frame is created, it has to be attached to an enclosing Window. Similarly, when the Window is destroyed, it must in turn destroy its Frame parts. Inheritance vs. Aggregation/Composition • Inheritance describes ‘is a’ / ‘is a kind of’ relationship between classes (base class - derived class) whereas aggregation describes ‘has a’ relationship between classes. Inheritance means that the object of the derived class inherits the properties of the base class; aggregation means that the object of the whole has objects of the part. For example, the relation “cash payment is a kind of payment” is modeled using inheritance; “purchase 72 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal In addition, in composition, the whole has the responsibility for the disposition of all its parts, i.e. for their creation and destruction.

ISBN : 978-81-963532-2-3 (E-Book) order has a few items” is modeled using aggregation. • Inheritance is used to model a “generic-specific” relationship between classes whereas aggregation/composition is used to model a “whole-part” relationship between classes. • Inheritance means that the objects of the subclass can be used anywhere the super class may appear, but not the reverse; i.e. wherever we could use instances of ‘payment’ in the system, we could substitute it with instances of ‘cash payment’, but the reverse cannot be done. • Inheritance is defined statically. It cannot be changed at run-time. Aggregation is defined dynamically and can be changed at run-time. Aggregation is used when the type of the object can change over time. For example, consider this situation in a business system. A BusinessPartner might be a Customer or a Supplier or both. Initially we might be tempted to model it as in Fig 14.4(a). But in fact, during its lifetime, a business partner might become a customer as well as a supplier, or it might change from one to the other. In such cases, we prefer aggregation instead (see Fig 14.4(b). Here, a business partner is a Customer if it has an aggregated Customer object, a Supplier if it has an aggregated Supplier object and a "Customer_Supplier" if it has both. Here, we have only two types. Hence, we are able to model it as inheritance. But what if there were several different types and combinations thereof? The inheritance tree would be absolutely incomprehensible. Also, the aggregation model allows the possibility for a business partner to be neither - i.e. has neither a customer nor a supplier object aggregated with it. 73 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) Fig.a) Representation of BusinessPartner, Customer, Supplier relationship using inheritance 74 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) Fig.b) Representation of BusinessPartner, Customer, Supplier relationship using aggregation • The advantage of aggregation is the integrity of encapsulation. The operations of an object are the interfaces of other objects which imply low implementation dependencies. The significant disadvantage of aggregation is the increase in the number of objects and their relationships. On the other hand, inheritance allows for an easy way to modify implementation for reusability. But the significant disadvantage is that it breaks encapsulation, which implies implementation dependence. ACTIVITYANDSTATECHARTDIAGRAM Theactivitydiagramispossiblyonemodelingelementwhichwasnotpresentinanyofthepredec essors of UML. No such diagrams were present either in the works of Booch, Jacobson, orRumbaugh. It is possibly based on the event diagram of Odell [1992] through the notation is verydifferent from that used by Odell. The activity diagram focuses on representing activities or chunksof processing which may or may not correspond to the methods of classes. An activity is a state withan internal action and one or moreoutgoing transitions which automatically follow the terminationof the internal activity. If an activity has more than one outgoing transitions, then these must beidentified through conditions. An interesting feature of the activity diagrams is the swim lanes. Swimlanes enable you to group activities based on who is performing them, e.g. academic department vs.hostel office. Thus swim lanes subdivide activities based on the responsibilities of some components.Theactivitiesinaswimlanecanbeassignedtosomemodelelements,e.g.classesor somecomponent,etc. Activity diagrams arenormally employed in business process modeling. This is carried out duringthe initial stages of requirements analysis and specification. Activity diagrams can be very useful tounderstand complex processing activities involving many components. Later these diagrams can beusedtodevelopinteractiondiagramswhichhelptoallocateactivities(responsibilities)toclas ses. Thestudentadmissionprocessinauniversityisshownasanactivitydiagraminfig.16.1.Thissho 75 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) ws the part played by different components of the Institute in the admission procedure. After thefees are received at the account section, parallel activities start at the hostel office, hospital, and theDepartment. After all these activities complete (this synchronization is represented as a horizontalline),theidentitycard canbe issued to astudent bythe Academicsection. Fig. Activitydiagramforstudent admission procedure at a university 76 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) Activitydiagramsvs.proceduralflow charts Activity diagrams are similar to the procedural flow charts. The difference is that activity diagramssupportdescriptionofparallelactivitiesandsynchronizationaspectsinvolvedindiffe rentactivities. STATECHARTDIAGRAM A state chart diagram is normally used to model how the state of an object changes in its lifetime.State chart diagrams are good at describing how the behavior of an object changes across several usecase executions. However, if we are interested in modeling some behavior that involves severalobjects collaborating with each other, state chart diagram is not appropriate. State chart diagrams arebasedon the finite statemachine (FSM)formalism. A FSM consists of a finite number of states corresponding to those of the object being modeled. Theobject undergoes state changes when specific events occur. The FSM formalism existed long beforethe object-oriented technology and has been used for a wide variety of applications. Apart frommodeling,ithasevenbeenusedintheoreticalcomputerscienceasageneratorforregularlan guages. A major disadvantage of the FSM formalism is the state explosion problem. The number of statesbecomes too many and the model too complex when used tomodel practical systems. This problemis overcome in UML by using state charts. The state chart formalism was proposed by David Harel[1990]. A state chart is a hierarchical model of a system and introduces the concept of a compositestate(alsocalled nested state). Actions are associated with transitions and are considered to be processes that occur quickly and arenotinterruptible.Activitiesareassociatedwithstatesandcantakelonger.Anactivity canbeinterruptedbyan event. Thebasicelementsofthestatechartdiagramareasfollows:  Initialstate-Thisisrepresentedasafilledcircle. Software Engineering Keerthana P, Manasa KN, Ganga D Bengal 77 ISBN : 978-81-963532-2-3 (E-Book)    Finalstate-Thisisrepresentedbyafilledcircleinsidealargercircle. State-Thesearerepresentedbyrectangleswithroundedcorners. Transition- A transition is shown as an arrow between two states. Normally, the name of theevent which causes the transition is places alongside the arrow. A guard to the transition canalso be assigned. A guard is a Boolean logic condition. The transition can take place only ifthe grade evaluates to true. The syntax for the label of the transition is shown in 3 parts: event[guard]/action. An example state chart for the order object of the Trade House Automation software is shown in fig.16.2. 78 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) Fig.State chartdiagramforanorder object Activitydiagramvs.Statechart diagram  flowofcontrolfromstatetostate.  Both activity and state chart diagrams model the dynamic behavior of the system. Activitydiagram is essentially a flowchart showing flow of control from activity to activity. A statechartdiagramshowsastatemachineemphasizingthe An activity diagram is a special case of a state chart diagram in which all or most of the statesare activity states and all or most of the transitions are triggered by completion of activities executionwithina statemachine). inthesourcestate(Anactivityisanongoingnon-atomic 79 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book)  Activity diagrams may stand alone to visualize, specify, and document the dynamics of asociety of objects or they may be used to model the flow of control of an operation. Statechart diagrams may be attached to classes, use cases, or entire systems in order to visualize,specify,and document thedynamicsofan individualobject. MODULE-3 Coding- The objective of the coding phase is to transform the design of a system into code in a high level language and then to unit test this code. The programmers adhere to standard and well defined style of coding which they call their coding standard. The main advantages of adhering to a standard style of coding are as follows: • A coding standard gives uniform appearances to the code written by different engineers • It facilitates code of understanding. • Promotes good programming practices. For implementing our design into a code, we require a good high level language. A programming language should have the following features: Characteristics of a Programming Language • Readability: A good high-level language will allow programs to be written in some ways that resemble a quite-English description of the underlying algorithms. If care is taken, the coding may be done in a way that is essentially self-documenting. • Portability: High-level languages, being essentially machine independent, should be able to develop portable software. • Generality: Most high-level languages allow the writing of a wide variety of programs, thus relieving the programmer of the need to become expert in many diverse languages. • Brevity: Language should have the ability to implement the algorithm with less amount of code. Programs expressed in high-level languages are often considerably shorter than their low-level equivalents. • Error checking: Being human, a programmer is likely to make many mistakes in the 80 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) development of a computer program. Many high-level languages enforce a great deal of error checking both at compile-time and at run-time. • Cost: The ultimate cost of a programming language is a function of many of its characteristics. • Familiar notation: A language should have familiar notation, so it can be understood by most of the programmers. • Quick translation: It should admit quick translation. • Efficiency: It should permit the generation of efficient object code. • Modularity: It is desirable that programs can be developed in the language as a collection of separately compiled modules, with appropriate mechanisms for ensuring selfconsistency between these modules. • Widely available: Language should be widely available and it should be possible to provide translators for all the major machines and for all the major operating systems. A coding standard lists several rules to be followed during coding, such as the way variables are to be named, the way the code is to be laid out, error return conventions, etc. Coding standards and guidelines Good software development organizations usually develop their own coding standards and guidelines depending on what best suits their organization and the type of products they develop. The following are some representative coding standards. 1. Rules for limiting the use of global: These rules list what types of data can be declared global and what cannot. 2. Contents of the headers preceding codes for different modules: The information contained in the headers of different modules should be standard for an organization. The 81 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) exact format in which the header information is organized in the header can also be specified. The following are some standard header data: • Name of the module. • Date on which the module was created. • Author’s name. • Modification history. • Synopsis of the module. • Different functions supported, along with their input/output parameters. • Global variables accessed/modified by the module. 3. Naming conventions for global variables, local variables, and constant identifiers: A possible naming convention can be that global variable names always start with a capital letter, local variable names are made of small letters, and constant names are always capital letters. 4. Error return conventions and exception handling mechanisms: The way error conditions are reported by different functions in a program are handled should be standard within an organization. For example, different functions while encountering an error condition should either return a 0 or 1 consistently. The following are some representative coding guidelines recommended by many software development organizations. 1. Do not use a coding style that is too clever or too difficult to understand: Code should be easy to understand. Many inexperienced engineers actually take pride in writing cryptic and incomprehensible code. Clever coding can obscure meaning of the code and hamper understanding. It also makes maintenance difficult. 2. Avoid obscure side effects: The side effects of a function call include modification of parameters passed by reference, modification of global variables, and I/O operations. An obscure side effect is one that is not obvious from a casual examination of the code. Obscure side effects make it difficult to understand a piece of code. For example, if a global variable is changed obscurely in a called module or some file I/O is performed 82 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) which is difficult to infer from the function’s name and header information, it becomes difficult for anybody trying to understand the code. 3. Do not use an identifier for multiple purposes: Programmers often use the same identifier to denote several temporary entities. For example, some programmers use a temporary loop variable for computing and a storing the final result. The rationale that is usually given by these programmers for such multiple uses of variables is memory efficiency, e.g. three variables use up three memory locations, whereas the same variable used in three different ways uses just one memory location. However, there are several things wrong with this approach and hence should be avoided. Some of the problems caused by use of variables for multiple purposes as follows: • Each variable should be given a descriptive name indicating its purpose. This is not possible if an identifier is used for multiple purposes. Use of a variable for multiple purposes can lead to confusion and make it difficult for somebody trying to read and understand the code. • Use of variables for multiple purposes usually makes future enhancements more difficult. 4. The code should be well-documented: As a rule of thumb, there must be at least one comment line on the average for every three-source line. 5. The length of any function should not exceed 10 source lines: A function that is very lengthy is usually very difficult to understand as it probably carries out many different functions. For the same reason, lengthy functions are likely to have disproportionately larger number of bugs. 6. Do not use goto statements: Use of goto statements makes a program unstructured and very difficult to understand. Code Review Code review for a model is carried out after the module is successfully compiled and the all the syntax errors have been eliminated. Code reviews are extremely cost-effective 83 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) strategies for reduction in coding errors and to produce high quality code. Normally, two types of reviews are carried out on the code of a module. These two types code review techniques are code inspection and code walk through. Code Walk Throughs Code walk through is an informal code analysis technique. In this technique, after a module has been coded, successfully compiled and all syntax errors eliminated. A few members of the development team are given the code few days before the walk through meeting to read and understand code. Each member selects some test cases and simulates execution of the code by hand (i.e. trace execution through each statement and function execution). The main objectives of the walk through are to discover the algorithmic and logical errors in the code. The members note down their findings to discuss these in a walk through meeting where the coder of the module is present. Even though a code walk through is an informal analysis technique, several guidelines have evolved over the years for making this naïve but useful analysis technique more effective. Of course, these guidelines are based on personal experience, common sense, and several subjective factors. Therefore, these guidelines should be considered as examples rather than accepted as rules to be applied dogmatically. Some of these guidelines are the following: • The team performing code walk through should not be either too big or too small. Ideally, it should consist of between three to seven members. • Discussion should focus on discovery of errors and not on how to fix the discovered errors. • Inder to foster cooperation and to avoid the feeling among engineers that they are being evaluated in the code walk through meeting, managers should not attend the walk through meetings. 84 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) Code Inspection In contrast to code walk through, the aim of code inspection is to discover some common types of errors caused due to oversight and improper programming. In other words, during code inspection the code is examined for the presence of certain kinds of errors, in contrast to the hand simulation of code execution done in code walk throughs. For instance, consider the classical error of writing a procedure that modifies a formal parameter while the calling routine calls that procedure with a constant actual parameter. It is more likely that such an error will be discovered by looking for these kinds of mistakes in the code, rather than by simply hand simulating execution of the procedure. In addition to the commonly made errors, adherence to coding standards is also checked during code inspection. Good software development companies collect statistics regarding different types of errors commonly committed by their engineers and identify the type of errors most frequently committed. Such a list of commonly committed errors can be used during code inspection to look out for possible errors. Following is a list of some classical programming errors which can be checked during code inspection: • Use of uninitialized variables. • Jumps into loops. • Nonterminating loops. • Incompatible assignments. • Array indices out of bounds. • Improper storage allocation and deallocation. • Mismatches between actual and formal parameter in procedure calls. • Use of incorrect logical operators or incorrect precedence among operators. • Improper modification of loop variables. • Comparison of equally of floating point variables, etc. Clean Room Testing Clean room testing was pioneered by IBM. This type of testing relies heavily on walk throughs, inspection, and formal verification. The programmers are not allowed to test any of their code by executing the code other than doing some syntax testing using a compiler. The software development philosophy is based on avoiding software defects by using a rigorous inspection process. The objective of this software is zero-defect software. The 85 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) name ‘clean room’ was derived from the analogy with semi-conductor fabrication units. In these units (clean rooms), defects are avoided by manufacturing in ultra-clean atmosphere. In this kind of development, inspections to check the consistency of the components with their specifications has replaced unit-testing. This technique reportedly produces documentation and code that is more reliable and maintainable than other development methods relying heavily on code execution-based testing. The clean room approach to software development is based on five characteristics: • Formal specification: The software to be developed is formally specified. A statetransition model which shows system responses to stimuli is used to express the specification. • Incremental development: The software is partitioned into increments which are developed and validated separately using the clean room process. These increments are specified, with customer input, at an early stage in the process. • Structured programming: Only a limited number of control and data abstraction constructs are used. The program development process is process of stepwise refinement of the specification. • Static verification: The developed software is statically verified using rigorous software inspections. There is no unit or module testing process for code components • Statistical testing of the system: The integrated software increment is tested statistically to determine its reliability. These statistical tests are based on the operational profile which is developed in parallel with the system specification. The main problem with this approach is that testing effort is increased as walk throughs, inspection, and verification are time-consuming. Software Documentation When various kinds of software products are developed then not only the executable files and the source code are developed but also various kinds of documents such as users’ manual, software requirements specification (SRS) documents, design documents, test documents, installation manual, etc are also developed as part of any software engineering process. All these documents are a vital part of good software development practice. Good 86 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) documents are very useful and server the following purposes: o Good documents enhance understandability and maintainability of a software product. They reduce the effort and time required for maintenance. o Use documents help the users in effectively using the system. o Good documents help in effectively handling the manpower turnover problem. Even when an engineer leaves the organization, and a new engineer comes in, he can build up the required knowledge easily. o Production of good documents helps the manager in effectively tracking the progress of the project. The project manager knows that measurable progress is achieved if a piece of work is done and the required documents have been produced and reviewed. Different types of software documents can broadly be classified into the following: • Internal documentation • External documentation Internal documentation is the code comprehension features provided as part of the source code itself. Internal documentation is provided through appropriate module headers and comments embedded in the source code. Internal documentation is also provided through the useful variable names, module and function headers, code indentation, code structuring, use of enumerated types and constant identifiers, use of user-defined data types, etc. Careful experiments suggest that out of all types of internal documentation meaningful variable names is most useful in understanding the code. This is of course in contrast to the common expectation that code commenting would be the most useful. The research finding is obviously true when comments are written without thought. For example, the following style of code commenting does not in any way help in understanding the code. a = 10; /* a made 10 */ But even when code is carefully commented, meaningful variable names still are more helpful in understanding a piece of code. Good software development organizations usually ensure good internal documentation by appropriately formulating their coding standards and coding guidelines. External documentation is provided through various types of supporting documents such 87 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) as users’ manual, software requirements specification document, design document, test documents, etc. A systematic software development style ensures that all these documents are produced in an orderly fashion. TESTING Program Testing Testing a program consists of providing the program with a set of test inputs (or test cases) and observing if the program behaves as expected. If the program fails to behave as expected, then the conditions under which failure occurs are noted for later debugging and correction. Some commonly used terms associated with testing are:  Failure: This is a manifestation of an error (or defect or bug). But, the mere presence of an error may not necessarily lead to a failure.  Test case: This is the triplet [I,S,O], where I is the data input to the system, S is the state of the system at which the data is input, and O is the expected output of the system.  Test suite: This is the set of all test cases with which a given software product is to be tested. Aim of Testing The aim of the testing process is to identify all defects existing in a software product. However for most practical systems, even after satisfactorily carrying out the testing phase, it is not possible to guarantee that the software is error free. This is because of the fact that the input data domain of most software products is very large. It is not practical to test the software exhaustively with respect to each value that the input data may assume. Even with this practical limitation of the testing process, the importance of testing should not be underestimated. It must be remembered that testing does expose many defects existing in a software product. Thus testing provides a practical way of reducing defects in 88 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) a system and increasing the users’ confidence in a developed system. Verification Vs Validation Verification is the process of determining whether the output of one phase of software development conforms to that of its previous phase, whereas validation is the process of determining whether a fully developed system conforms to its requirements specification. Thus while verification is concerned with phase containment of errors, the aim of validation is that the final product be error free. Design of Test Cases Exhaustive testing of almost any non-trivial system is impractical due to the fact that the domain of input data values to most practical software systems is either extremely large or infinite. Therefore, we must design an optional test suite that is of reasonable size and can uncover as many errors existing in the system as possible. Actually, if test cases are selected randomly, many of these randomly selected test cases do not contribute to the significance of the test suite, i.e. they do not detect any additional defects not already being detected by other test cases in the suite. Thus, the number of random test cases in a test suite is, in general, not an indication of the effectiveness of the testing. In other words, testing a system using a large collection of test cases that are selected at random does not guarantee that all (or even most) of the errors in the system will be uncovered. Consider the following example code segment which finds the greater of two integer values x and y. This code segment has a simple programming error. if (x>y) max = x; else max = x; For the above code segment, the test suite, {(x=3,y=2);(x=2,y=3)} can detect the error, whereas a larger test suite {(x=3,y=2);(x=4,y=3);(x=5,y=1)} does not detect the error. So, it would be incorrect to say that a larger test suite would always detect more errors than a smaller one, unless of course the larger test suite has also been carefully designed. This implies that the test suite should be carefully designed than picked randomly. Therefore, 89 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) systematic approaches should be followed to design an optimal test suite. In an optimal test suite, each test case is designed to detect different errors. Functional Testing Vs. Structural Testing In the black-box testing approach, test cases are designed using only the functional specification of the software, i.e. without any knowledge of the internal structure of the software. For this reason, black-box testing is known as functional testing. On the other hand, in the white-box testing approach, designing test cases requires thorough knowledge about the internal structure of software, and therefore the white-box testing is called structural testing. Fig.Unit testingwiththehelp of driverandstub modules BLACK-BOX TESTING Testing in the large vs. testing in the small Software products are normally tested first at the individual component (or unit) level. This is referred to as testing in the small. After testing all the components individually, the components are slowly integrated and tested at each level of integration (integration testing). Finally, the fully integrated system is tested (called system testing). Integration and system testing are known as testing in the large. 90 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) Unit Testing Unit testing is undertaken after a module has been coded and successfully reviewed. Unit testing (or module testing) is the testing of different units (or modules) of a system in isolation. In order to test a single module, a complete environment is needed to provide all that is necessary for execution of the module. That is, besides the module under test itself, the following steps are needed in order to be able to test the module: • The procedures belonging to other modules that the module under test calls. • Nonlocal data structures that the module accesses. • A procedure to call the functions of the module under test with appropriate parameters. Modules are required to provide the necessary environment (which either call or are called by the module under test) is usually not available until they too have been unit tested, stubs and drivers are designed to provide the complete environment for a module. The role of stub and driver modules is pictorially shown in fig. A stub procedure is a dummy procedure that has the same I/O parameters as the given procedure but has a highly simplified behavior. For example, a stub procedure may produce the expected behavior using a simple table lookup mechanism. A driver module contain the nonlocal data structures accessed by the module under test, and would also have the code to call the different functions of the module with appropriate parameter values. white-box testing 91 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) One white-box testing strategy is said to be stronger than another strategy, if all types of errorsdetected by thefirst testing strategy is also detected by the second testing strategy, and thesecondtestingstrategyadditionallydetectssomemoretypesoferrors.Whentwotestingstrategiesdet ecterrorsthataredifferentatleastwithrespecttosometypesoferrors,thentheyare called complementary. The concepts of stronger and complementary testing are schematicallyillustratedin fig.. Fig.Strongerand complementarytestingstrategies StatementCoverage The statement coverage strategy aims to design test cases so that every statement in a program isexecutedatleastonce.The principalidea governing thestatementcoveragestrategy isthatunless a statement is executed, it is very hard to determine if an error exists in that statement.Unlessastatementisexecuted,itisverydifficulttoobservewhetheritcausesfailureduetosome illegal memory access, wrong result computation, etc. However, executing some statementonceandobservingthatitbehavesproperlyforthatinputvalueisnoguaranteethatitwillbehavecorre ctlyforallinputvalues.Inthefollowing,designingof testcasesusingthestatementcoveragestrategyhavebeen shown. Example:ConsidertheEuclid’sGCDcomputationalgorithm:intc ompute_gcd(x,y) 92 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) intx, y; { 1while(x!=y) { 2if(x>y)then 3x=x– y;4elsey=y–x; 5 } 6return x; } By choosing the test set {(x=3, y=3), (x=4, y=3), (x=3, y=4)}, we can exercise the program suchthatall statements are executed at least once. BranchCoverage In the branch coverage-based testing strategy, test cases are designed to make each branchcondition to assume true and false values in turn. Branch testing is also known as edge testing asinthistestingscheme,eachedgeof aprogram’scontrolflow graphistraversedatleast once. It is obvious that branch testing guarantees statement coverage and thus is a stronger testingstrategycomparedtothestatementcoveragebasedtesting.ForEuclid’sGCDcomputationalgorithm, the test cases for branch coverage can be {(x=3, y=3), (x=3, y=2), (x=4, y=3), (x=3,y=4)}. ConditionCoverage Inthisstructuraltesting,testcasesaredesignedtomakeeachcomponentofacompositeconditional expression to assume both true and false values. For example, in the conditionalexpression ((c1.and.c2).or.c3), the components c1, c2 and c3 are each made to assume both trueand falsevalues.Branchtesting is probably thesimplest condition testing strategy where onlythe compound conditions appearing in the different branch statements are made to assume thetrue and false values. Thus, condition testing is a stronger testing strategy than branch testing andbranchtestingisstrongertestingstrategy thanthestatementcoverage-basedtesting.Foracomposite conditional expression of n components, for condition coverage, 2ⁿ test cases arerequired. Thus, for condition coverage, the number of test cases increases exponentially with thenumber of component conditions. Therefore, a condition coverage-based testing technique ispractical onlyif n (thenumberof conditions) is small. PathCoverage The path coverage-based testing strategy requires us to design test cases such that all linearlyindependent paths in the program are executed at least once. A linearly independent path 93 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) can bedefinedin terms ofthecontrol flow graph (CFG)ofaprogram. ControlFlow Graph(CFG) A control flow graph describes the sequence in which the different instructions of a program getexecuted. In other words, a control flow graph describes how the control flows through theprogram. In order to draw the control flow graph of a program, all the statements of a programmust be numbered first. The different numbered statements serve as nodes of the control flowgraph (as shown in fig. 20.2). An edge from one node to another node exists if the execution ofthestatementrepresentingthefirst nodecanresultinthetransferof controltotheothernode. The CFG for any program can be easily drawn by knowing how to represent the sequence,selection, and iteration type of statements in the CFG. After all, a program is made up from thesetypes of statements. Fig. 20.2summarizes how the CFG for these three types of statements canbe drawn. It is important to note that for the iteration type of constructs such as the whileconstruct, the loop condition is tested only at the beginning of the loop and therefore the controlflow from the last statement of the loop is always to the top of the loop. Using these basic ideas,theCFGofEuclid’sGCDcomputationalgorithmcan bedrawnas shown infig. 20.3. Sequence: a=5; b=a*2-1; Fig.(a):CFGforsequenceconstructs Selection: if(a>b) c=3; else 94 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) c=5; c=c*c; Fig.(b):CFG forselectionconstructs Iteration: while(a>b) { b=b-1;b=b*a; } c=a+b; Fig.(c):CFGfor anditerationtypeofconstructs 95 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) EUCLID’SGCDComputationAlgorithm Fig.:Controlflowdiagram Path A path through a program is a node and edge sequence from the starting node to a terminal nodeof the control flow graph of a program. There can be more than one terminal node in a 96 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) program.Writing test cases to cover all the paths of a typical program is impractical. For this reason, coveragetestingdoesnotrequirecoverageofallpathsbutonlycoverageoflinearlyindependentpaths. Linearlyindependentpath Alinearlyindependentpathisanypaththroughtheprogramthatintroducesatleastonenewedge that is not included in any other linearly independent paths. If a path has one new nodecompared to all other linearly independent paths, then the path is also linearly independent. This isbecause;anypathhavinganewnodeautomaticallyimpliesthatithasanewedge.Thus,apaththatissub-path ofanother pathisnot considered tobe alinearlyindependent path. ControlFlowGraph Inordertounderstandthepathcoverage-basedtestingstrategy,itisverymuchnecessarytounderstand the control flow graph (CFG) of a program. Control flow graph (CFG) of a program hasbeendiscussed earlier. LinearlyIndependentPath The path-coverage testing does not require coverage of all paths but only coverage of linearlyindependentpaths. Linearlyindependent pathshavebeen discussedearlier. CyclomaticComplexity For more complicated programs it is not easy to determine the number of independent paths of theprogram.McCabe’scyclomaticcomplexity definesanupperboundforthenumberoflinearlyindependent paths through a program. Also, the McCabe’s cyclomatic complexity is very simple tocompute. Thus, the McCabe’s cyclomatic complexity metric provides a practical way of determiningthe maximum number of linearly independent paths in a program. Though the McCabe’s metric doesnot directly identify the linearly independent paths, but it informs approximately how many paths tolookfor. There are three different ways to compute the cyclomatic complexity. The answers computed by thethreemethodsare guaranteed toagree. thepathMethod1: GivenacontrolflowgraphGofaprogram,thecyclomaticcomplexityV(G)canbecomputedas: 97 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) V(G)= E– N + 2 where N is the number of nodes of the control flow graph and E is the number of edges in thecontrolflowgraph. FortheCFGofexampleshowninfig.20.3,E=7andN=6.Therefore,thecyclomaticcomplexity= 76+2=3. Method2: An alternative way of computing the cyclomatic complexity of a program from an inspectionofitscontrol flowgraph isasfollows: V(G)=Total numberof boundedareas+1 In the program’s control flow graph G, any region enclosed by nodes and edges can be calledasaboundedarea.Thisisan easywayto determinetheMcCabe’scyclomaticcomplexity. But, what if the graph G is not planar, i.e. however you draw the graph, two or more edgesintersect? Actually, it can be shown that structured programs always yield planar graphs. But,presenceofGOTO’scaneasilyaddintersectingedges.Therefore,fornonstructuredprograms,thiswayofcomputingtheMcCabe’scyclomaticcomplexitycannotbe used. Thenumberofboundedareasincreaseswiththenumberofdecisionpathsandloops.Therefore, the McCabe’s metric provides a quantitative measure of testing difficulty and theultimate reliability. Forthe CFG example shown in fig. 20.3, from a visual examination ofthe CFG the number of bounded areas is 2. Therefore the cyclomatic complexity, computingwith this method is also 2+1 = 3. This method provides a very easy way of computing thecyclomatic complexity of CFGs, just from a visual examination of the CFG. On the otherhand, the other method of computing CFGs is more amenable to automation, i.e. it can beeasily coded into a program which can be used to determine the cyclomatic complexities ofarbitraryCFGs. Method3: The cyclomatic complexity of a program can also be easily computed by computing thenumber of decision statements of the program. If N is the number of decision statement of aprogram,thenthe McCabe’smetricisequalto N+1. DataFlow-BasedTesting Data flow-based testing method selects test paths of a program according to the locations of thedefinitionsand usesofdifferent variablesina program. ForastatementnumberedS,let DEF(S)={X/statementScontainsadefinitionofX},andUSE 98 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) S(S)={X/statementScontainsause ofX} For the statement S:a=b+c;, DEF(S) = {a}. USES(S) = {b,c}. The definition of variable X atstatement S is said to be live at statement S1, if there exists a path from statement S to statement S1whichdoesnot contain anydefinition ofX. The definition-use chain (or DU chain) of a variable X is of form [X, S, S1], where S and S1 arestatement numbers, such that X Є DEF(S) and X Є USES(S1), and the definition of X in thestatement S is live at statement S1. One simple data flow testing strategy is to require that every DUchain be covered at least once. Data flow testing strategies are useful for selecting test paths of aprogramcontainingnestedifand loop statements. MutationTesting In mutation testing, the software is first tested by using an initial test suite built up from the differentwhitebox testing strategies. After theinitial testing is complete, mutation testing is taken up. Theidea behind mutation testing is to make few arbitrary changes to a program at a time. Each time theprogram is changed, it is called as a mutated program and the change effected is called as a mutant. Amutated programis tested againstthefull testsuite of theprogram.If there exists atleast one testcase in the test suite for which a mutant gives an incorrect result, then the mutant is said to be dead. Ifa mutant remains alive even after all the test cases have been exhausted, the test data is enhanced tokill the mutant. The process of generation and killing of mutants can be automated by predefining asetofprimitivechangesthatcanbeappliedtotheprogram.Theseprimitivechangescanbealterations such as changing an arithmetic operator, changing the value of a constant, changing a datatype, etc. A major disadvantage of the mutation-based testing approach is that it is computationallyveryexpensive, since alarge number ofpossible mutantscanbe generated. Sincemutationtestinggeneratesalargenumberofmutantsandrequiresustocheckeachmutantwith the full test suite, it is not suitable for manual testing. Mutation testing should be used inconjunctionofsome testingtoolwhich wouldrun allthetest casesautomatically. DEBUGGING,INTEGRATION ANDSYSTEMTESTING NeedforDebugging Once errors are identified in a program code, it is necessary to first identify the precise programstatements responsible for the errors and then to fix them. Identifying errors in a program codeandthen fixthem up are known as debugging. DebuggingApproaches 99 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) Thefollowingaresomeoftheapproachespopularlyadopted byprogrammers fordebugging. BruteForceMethod: This is the most common method of debugging but is the least efficient method. In thisapproach,theprogramisloadedwithprintstatementstoprinttheintermediatevalueswith the hope that some of the printed values will help to identify the statement in error.This approach becomes more systematic with the use of a symbolic debugger (also calleda source code debugger), because values of different variables can be easily checked andbreakpoints andwatch pointscan beeasilyset totest thevalues ofvariables effortlessly. Backtracking: This is also a fairly common approach. In this approach, beginning from the statement atwhich an error symptom has been observed, the source code is traced backwards until theerrorisdiscovered.Unfortunately,asthenumberofsourcelinestobetracedbackincreases,the numberofpotentialbackwardpathsincreasesandmaybecomeunmanageablylargethuslimiting the useof this approach. CauseEliminationMethod: In this approach, a list of causes which could possibly have contributed to the errorsymptom is developed and tests are conducted to eliminate each. A related technique ofidentificationofthe errorfromthe errorsymptomis thesoftwarefault tree analysis. ProgramSlicing: This technique is similar to back tracking. Here the search space is reduced by definingslices. A slice of a program for a particular variable at a particular statement is the set ofsourcelines precedingthis statement that can influencethevalue ofthat variable DebuggingGuidelines Debugging is often carried out by programmers based on their ingenuity. The following are somegeneralguidelinesforeffectivedebugging:  Many times debugging requires a thorough understanding of the program design. Tryingto debug based on a partial understanding of the system design and implementation mayrequirean inordinateamount ofeffort tobeputinto debuggingeven simpleproblems.  Debugging may sometimes even require full redesign of the system. In such cases, acommon mistake that novice programmers often make is attempting not to fix the errorbutits symptoms.  One must be beware of the possibility that an error correction may introduce new errors.Thereforeaftereveryround oferror-fixing, regressiontestingmustbecarriedout. 100 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) ProgramAnalysis Tools A program analysis tool means an automated tool that takes the source code or the executablecodeofaprogramasinputandproducesreportsregardingseveralimportantcharacteristicsoft he program, such as its size, complexity, adequacy of commenting, adherence to programmingstandards,etc. Wecan classifytheseintotwo broadcategories ofprogram analysis tools:  StaticAnalysistools  DynamicAnalysistools  Staticprogramanalysistools StaticAnalysisToolisalsoaprogramanalysistool.Itassessesandcomputesvariouscharacteristics of a software product without executing it. Typically, static analysis tools analyzesome structural representation of a program to arrive at certain analytical conclusions, e.g. thatsomestructural properties hold. Thestructural properties that areusuallyanalyzed are:  Whetherthecodingstandards havebeenadheredto?  Certainprogrammingerrorssuchasuninitializedvariablesandmismatchbetweenact ualandformalparameters,variablesthataredeclaredbutneverusedare also checked. Codewalkthroughsandcodeinspectionsmightbeconsideredasstaticanalysismethods.But,the term static program analysis is used to denote automated analysis tools. So, a compiler can beconsideredto beastaticprogram analysis tool. Dynamic program analysis tools - Dynamic program analysis techniques require the program tobe executed and its actual behavior recorded. A dynamic analyzer usually instruments the code(i.e. adds additional statements in the source code to collect program execution traces). Theinstrumented code when executed allows us to record the behavior of the software for differenttestcases.Afterthesoftwarehasbeentestedwithitsfulltestsuiteanditsbehaviorrecorded,the 101 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) dynamic analysis tool caries out a post execution analysis and produces reports which describethe structural coverage that has been achieved by the complete test suite for the program. Forexample, the post execution dynamic analysis report might provide data on extent statement,branchand path coverageachieved. Normally the dynamic analysis results are reported in the form of a histogram or a pie chart todescribe the structural coverage achieved for different modules of the program. The output of adynamic analysis tool can be stored and printed easily and provides evidence that thoroughtesting has been done. The dynamic analysis results the extent of testing performed in white-boxmode. If the testing coverage is not satisfactory more test cases can be designed and added to thetest suite. Further, dynamic analysis results can help to eliminate redundant test cases from thetest suite. 102 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) 23 INTEGRATIONTESTING Theprimaryobjectiveofintegrationtestingistotestthemoduleinterfaces,i.e.therearenoerrors in the parameter passing, when one module invokes another module. During integrationtesting, different modules of a system are integrated in a planned manner using an integrationplan. The integration plan specifies the steps and the order in which modules are combined torealize the full system. After each integration step, the partially integrated system is tested. Animportant factor that guides the integration plan is the module dependency graph. The structurechart(ormoduledependencygraph)denotestheorderinwhichdifferentmodulescalleachother .Byexaminingthestructurechart theintegrationplancan bedeveloped. Integrationtestapproaches There are four types of integration testing approaches. Any one (or a mixture) of the followingapproachescanbeusedtodevelop arethefollowing: theintegrationtestplan.Thoseapproaches  Bigbangapproach  Bottom-upapproach  Top-downapproach  Mixed-approach Big-BangIntegrationTesting It is the simplest integration testing approach, where all the modules making up a system areintegrated in a single step. In simple words, all the modules of the system are simply put togetherandtested.However,thistechniqueispracticableonlyforverysmallsystems.Themainproblem with this approach is that once an error is found during the integration testing, it is verydifficult to localize the error as the error may potentially belong to any of the modules beingintegrated. Therefore, debugging errors reported during big bang integration testing are veryexpensive to fix. Bottom-UpIntegrationTesting In bottom-up testing, each subsystem is tested separately and then the full system is tested. Asubsystem might consist of many modules which communicate among each other through well-defined interfaces. The primary purpose of testing each subsystem is to test the interfaces amongvarious modules making up the subsystem. Both control and data interfaces are tested. 103 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) The testcases must be carefully chosen to exercise the interfaces in all possible manners Large softwaresystemsnormallyrequireseverallevelsofsubsystemtesting;lowerlevelsubsystemsaresuccessively combined to form higher-level subsystems. A principal advantage of bottom-upintegration testing is that several disjoint subsystems can be tested simultaneously. In a purebottom-up testing no stubs are required, only test-drivers are required. A disadvantage of bottom-up testing is the complexity that occurs when the system is made up of a large number of smallsubsystems.Theextreme casecorrespondsto thebig-bangapproach. Top-DownIntegrationTesting Top-down integration testing starts with the main routine and one or two subordinate routines inthe system. After the top-level ‘skeleton’ has been tested, the immediately subroutines of the‘skeleton’ are combined with it and tested. Top-down integration testing approach requires theuse of program stubs to simulate the effect of lower-level routines that are called by the routinesunder test. A pure top-down integration does not require any driver routines. A disadvantage ofthe top-down integration testing approach is that in the absence of lower-level routines, manytimes it may become difficult to exercise the top-level routines in the desired manner since thelower-levelroutines perform several low-levelfunctions such asI/O. MixedIntegration Testing A mixed (also called sandwiched) integration testing follows a combination of top-down andbottom-up testing approaches. In top-down approach, testing can start only after the toplevelmodules have been coded and unit tested. Similarly, bottom-up testing can start only after thebottom level modules are ready. The mixed approach overcomes this shortcoming of the topdown and bottom-up approaches. In the mixed testing approaches, testing can start as and whenmodules become available. Therefore, this is one of the most commonly used integration testingapproaches. PhasedVs.Incremental Testing Thedifferentintegrationtestingstrategiesareeitherphasedorincremental.Acomparisonofthesetwo strategies is asfollows: o Inincrementalintegrationtesting,onlyonenewmoduleisaddedtothepartialsystemeach time. o Inphasedintegration,agroupofrelatedmodulesareaddedtothepartialsystemeachtime. Phasedintegrationrequireslessnumberofintegrationstepscomparedtotheincrementalintegrationappr oach.However,whenfailuresaredetected,itiseasiertodebugthesystemintheincrementaltestingapproa chsinceitisknownthattheerroriscausedbyadditionofasinglemodule. In fact, big bang testing is a degenerate case of the phased integration testing approach.Systemtesting Systemtestsaredesignedtovalidateafullydevelopedsystemtoassurethatitmeetsitsrequirements.There 104 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) are essentiallythreemain kinds of systemtesting:  AlphaTesting.Alphatestingreferstothesystemtestingcarriedoutbythetestteamwithinthe developingorganization.  Betatesting.Betatestingisthesystemtestingperformedbyaselectgroupoffriendlycustomers.  AcceptanceTesting.Acceptancetestingisthesystemtestingperformedbythecustomertodeter mine whether he should accept thedeliveryofthe system. 105 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) In each of the above types of tests, various kinds of test cases are designed by referring to theSRS document. Broadly, these tests can be classified into functionality and performance tests.The functionality test tests the functionality of the software to check whether it satisfies thefunctional requirements as documented in the SRS document. The performance test tests theconformanceof thesystem with thenonfunctional requirements of thesystem. PerformanceTesting Performancetestingiscarriedouttocheckwhetherthesystemneedsthenon-functionalrequirements identified in the SRS document. There are several types of performance testing.Among of them nine types are discussed below. The types of performance testing to be carriedoutonasystemdependonthedifferentnonfunctionalrequirementsofthesystemdocumentedintheSRS document. All performancetests can beconsidered as black-boxtests. • Stresstesting • Volumetesting • Configurationtesting • Compatibilitytesting • Regressiontesting • Recoverytesting • Maintenancetesting • Documentationtesting • Usabilitytesting StressTesting-Stresstestingisalsoknownasendurancetesting.Stresstestingevaluates system performance when it is stressed for short periods of time. Stress tests areblack box tests which are designed to impose a range of abnormal and even illegal inputconditions so as to stress the capabilities of the software. Input data volume, input datarate, processing time, utilization of memory, etc. are tested beyond the designed capacity.For example, suppose an operating system is supposed to support 15 multi programmedjobs, the system is stressed by attempting to run 15 or more jobs simultaneously. A real-time system might be tested to determine the effect of simultaneous arrival of severalhighpriorityinterrupts. Stress testing is especially important for systems that usually operate below the maximumcapacity but are severely stressed at some peak demand hours. For example, if the non-functional requirement specification states that the response time should not be more than20 secs per transaction when 60 concurrent users are working, then during the stresstestingthe response timeischecked with 60 usersworkingsimultaneously. 106 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) Volume Testing-It is especially important to check whether the data structures (arrays,queues,stacks,etc.)havebeendesignedtosuccessfullyextraordinarysituations.For Example, a compiler might be tested to check whether the symbol table overflows when averylargeprogram is compiled. ConfigurationTesting-Thisisusedtoanalyzesystembehaviorinvarioushardwareand software configurations specified in the requirements. Sometimes systems are built invariableconfigurationsfor different users. Forinstance, wemight define a minimalsystem to serve a single user, and other extension configurations to serve additional users.The system is configured in each of the required configurations and it is checked if thesystembehaves correctlyin all required configurations. Compatibility Testing -This type of testing is required when the system interfaces withother types of systems.Compatibility aims to check whether the interfacefunctionsperform as required. For instance, if the system needs to communicate with a largedatabase system to retrieve information, compatibility testing is required to test the speedandaccuracyof dataretrieval. Regression Testing -This type of testing is required when the system being tested is anupgradation of an already existing system to fix some bugs or enhance functionality,performance, etc. Regression testing is the practice of running an old test suite after eachchange to the system or after each bug fix to ensure that no new bug has been introduceddue to the change or the bug fix. However, if only a few statements are changed, then theentire testsuite need notbe run- only those testcasesthat testthe functionsthat arelikelyto be affected bythechangeneed to berun. Recovery Testing -Recovery testing tests the response of the system to the presence offaults, or loss of power, devices, services, data, etc. The system is subjected to the loss ofthe mentioned resources (as applicable and discussed in the SRS document) and it ischeckedifthesystemrecoverssatisfactorily.Forexample,theprintercanbedisconnected to check if the system hangs. Or, the power may be shut down to check theextentofdata loss and corruption. MaintenanceTesting-Thistestingaddressesthediagnosticprograms,andotherprocedures that are required to be developed to help maintenance of the system. It isverifiedthat theartifactsexist and theyperform properly. 107 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) DocumentationTesting-Itischeckedthattherequiredusermanual,maintenancemanuals, and technical manuals exist and are consistent. If the requirements specify thetypes of audience for which a specific manual should be designed, then the manual ischeckedfor compliance. Usability Testing- Usability testing concernschecking the user interface to see ifitmeets all user requirements concerning the user interface. During usability testing, thedisplayscreens,reportformats,andotheraspectsrelatingtotheuserinterfacerequirementsar etested. ErrorSeeding Sometimes the customer might specify the maximum number of allowable errors that may bepresent in the delivered system. These are often expressed in terms of maximum number ofallowable errors per line of source code. Errorseed can be used toestimate the number ofresidual errors in a system. Error seeding, as the name implies, seeds the code with some knownerrors. In other words, some artificial errors are introduced into the program artificially. Thenumberoftheseseedederrorsdetectedinthecourseofthestandardtestingprocedureisdetermined. These valuesinconjunctionwiththe number of unseedederrorsdetected can beusedto predict: • Thenumberof errors remainingin theproduct. • Theeffectivenessofthetestingstrategy. LetN bethe totalnumber ofdefects in thesystemand let n ofthesedefects be found bytesting.LetSbethe totalnumberof seededdefects, andlet sof thesedefectsbe foundduringtesting. n/N= s/S or N= S ×n/s Defectsstill remainingaftertesting = N–n= n×(S – s)/s Error seeding works satisfactorily only if the kind of seeded errors matches closely with the kindof defects that actually exist. However, it is difficult to predict the types of errors that exist in asoftware. To some extent, the different categories of errors that remain can be estimated to a firstapproximation by analyzing historical data of similar projects. Due to the shortcoming that thetypes of seeded errors should match closely with the types of errors actually existing in the code,errorseedingis useful onlyto a moderateextent. 108 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) RegressionTesting Regressiontestingdoesnotbelongtoeitherunittest,integrationtest,orsystemtesting.Instead,it is a separate dimension to these three forms of testing. The functionality of regression testinghasbeen discussedearlier. SOFTWAREMAINTENANCE NecessityofSoftwareMaintenance Softwaremaintenanceisbecominganimportantactivityofalargenumberofsoftwareorganizations. This is no surprise, given the rate of hardware obsolescence, the immortality of asoftware product per se, and the demand of the user community to see the existing softwareproducts run on newer platforms, run in newer environments, and/or with enhanced features.Whenthehardwareplatformischanged,andasoftwareproductperformssomelowlevelfunctions, maintenance is necessary. Also, whenever the support environment of a softwareproduct changes, the software product requires rework to cope up with the newer interface. Forinstance, a software product may need to be maintained when the operating system changes.Thus, every software product continues to evolve after its development through maintenanceefforts. Therefore it can be stated that software maintenance is needed to correct errors, enhancefeatures,port the softwareto newplatforms, etc. Typesofsoftwaremaintenance Therearebasicallythree types ofsoftwaremaintenance. Theseare:  Corrective: Corrective maintenance of a software product is necessary to rectify the bugsobservedwhile thesystem is in use.  Adaptive: A software product might need maintenance when the customers need theproduct to run on new platforms, on new operating systems, or when they need theproductto interfacewithnewhardwareor software.  Perfective: A software product needs maintenance to support the new features that userswant it to support, to change different functionalities of the system according to customerdemands,orto enhancetheperformanceof thesystem. Problemsassociatedwithsoftwaremaintenance Software maintenance work typically is much more expensive than what it should be and takesmore time than required.In software organizations, maintenance workismostly carried outusing ad hoc techniques. The primary reason being that software maintenance is one of the mostneglected areas of software engineering. Even though software maintenance is fast 109 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) becoming animportant area of work for many companies as the software products of yester years age, stillsoftware maintenance is mostly being carried out as fire-fighting operations, rather than throughsystematic and plannedactivities. Software maintenance has a very poor image in industry. Therefore, an organization often cannotemploy bright engineers to carry out maintenance work. Even though maintenance suffers from apoorimage,theworkinvolvedisoftenmorechallengingthandevelopmentwork.During maintenance it is necessary to thoroughly understand someone else’s work and then carry out therequiredmodifications and extensions. Another problem associated with maintenance work is that the majority of software productsneedingmaintenanceare legacyproducts. SoftwareReverseEngineering Softwarereverseengineeringistheprocessofrecoveringthedesignandtherequirementsspecification of a product from an analysis of its code. The purpose of reverse engineering is tofacilitate maintenance work by improving the understandability of a system and to produce thenecessary documents for a legacy system. Reverse engineering is becoming important, sincelegacy software products lack proper documentation, and are highly unstructured. Even welldesignedproductsbecomelegacysoftwareastheirstructuredegradesthroughaseriesofmaintenance efforts. The first stage of reverse engineering usually focuses on carrying out cosmetic changes to thecodetoimproveitsreadability,structure,andunderstandability,withoutchangingofitsfunctionalitie s. A process model for reverse engineering has been shown in fig. 24.1. A programcan be reformatted using any of the several available prettyprinter programs which layout theprogram neatly. Many legacy software products with complex control structure and unthoughtfulvariable names are difficult to comprehend. Assigning meaningful variable names is importantbecausemeaningfulvariablenamesarethemosthelpfulthingincodedocumentation.Allvaria bles, data structures, and functions should be assigned meaningful names wherever possible.Complexnestedconditionalsintheprogramcanbereplacedbysimplerconditionalstatementso rwhenever appropriatebycasestatements. 110 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) Fig.24.1:Aprocess modelforreverseengineering After the cosmetic changes have been carried out on a legacy software the process of extractingthe code, design, and the requirements specification can begin. These activities are schematicallyshown in fig. 24.2.Inorder toextract thedesign, afull understandingof thecodeis needed.Some automatic tools can be usedtoderive thedata flowand control flow diagramfrom thecode. The structure chart (module invocation sequence and data interchange among modules)shouldalsobeextracted.TheSRSdocumentcanbewrittenoncethefullcodehasbeenthoroughl yunderstood andthe designextracted. 111 ISBN : 978-81-963532-2-3 (E-Book) Fig.24.2:Cosmeticchangescarriedoutbeforereverseengineering Legacysoftwareproducts It isprudent todefine a legacy system asany software system that is hard to maintain.Thetypical problems associated with legacy systems are poor documentation, unstructured (spaghetticode with ugly control structure), and lack of personnel knowledgeable in the product. Many ofthe legacy systems were developed long time back. But, it is possible that a recently developedsystemhavingpoordesign and documentation canbeconsidered to be alegacysystem. The activities involved in a software maintenance project are not unique and depend on severalfactorssuchas: • theextentofmodificationto theproductrequired 112 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) • theresourcesavailable tothemaintenanceteam • theconditionsof theexistingproduct(e.g., howstructuredit is,how well documenteditis, etc.) • theexpectedprojectrisks,etc. When the changes needed to a software product are minor and straightforward, the code can bedirectlymodifiedandthechangesappropriatelyreflectedinallthedocuments.Butmoreelaborateacti vitiesarerequiredwhentherequiredchangesarenotsotrivial.Usually,forcomplex maintenance projects for legacy systems, the software process can be represented by areverse engineering cycle followed by a forward engineering cycle with an emphasis on as muchreuseas possiblefrom the existingcodeand otherdocuments. RELIABILITYGROWTHMODELS A reliability growth modelisa mathematicalmodelof how software reliability improvesaserrors are detected and repaired. A reliability growth model can be used to predict when (or if atall) a particular level of reliability is likely to be attained. Thus, reliability growth modeling canbe used to determine when to stop testing to attain a given reliability level. Although severaldifferent reliability growth models have been proposed, in this text we will discuss only two verysimplereliabilitygrowthmodels. Jelinski and Moranda Model -The simplest reliability growth model is a step function modelwhere it is assumed that the reliability increases by a constant increment each time an error isdetected and repaired. Such a model is shown in fig. 27.1. However, this simple model ofreliability which implicitly assumes that all errors contribute equally to reliability growth, ishighly unrealistic since it is already known that correction of different types of errors contributedifferentlyto reliabilitygrowth. 113 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) . Fig.27.1:Stepfunction modelofreliabilitygrowth Littlewood and Verall’s Model -This model allows for negative reliability growth to reflect thefact that when a repair is carried out, it may introduce additional errors. It also models the factthat as errors are repaired, the average improvement in reliability per repair decreases (Fig. 27.2).It treat’s an error’s contribution to reliability improvement to be an independent random variablehaving Gamma distribution. This distribution models the fact that error corrections with largecontributions to reliability growth are removed first. This represents diminishing return as testcontinues. Fig.27.2:Random-stepfunctionmodelofreliabilitygrowth StatisticalTesting Statistical testing is a testing process whose objective is to determine the reliability of softwareproducts rather than discovering errors. Test cases are designed for statistical testing with anentirelydifferent objectivethan thoseof conventional testing. 114 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) Operation profile Different categories of users may use a software for different purposes. For example, a Librarianmightusethelibraryautomationsoftwaretocreatememberrecords,addbookstothelibrary,etc. whereas a library member might use to software to query about the availability of the book,or to issue and return books. Formally, the operation profile of a software can be defined as theprobability distribution of the input of an average user. If the input to a number of classes {Ci} isdivided, the probability value of a class represent the probability of an average user selecting hisnext input from this class. Thus, the operation profile assigns a probability value Pi to each inputclass Ci. Stepsin statisticaltesting Statistical testing allows one to concentrate on testing those parts of the system that are mostlikely to be used. The first step of statistical testing is to determine the operation profile of thesoftware. The next step is to generate a set of test data corresponding to the determined operationprofile.Thethirdstepistoapplythetestcasestothesoftwareandrecordthetimebetweeneach failure. After a statistically significant number of failures have been observed, the reliability canbecomputed. Advantagesanddisadvantagesofstatisticaltesting Statistical testing allows one to concentrate on testing parts of the system that are most likely tobe used. Therefore, it results in a system that the users to be more reliable (than actually it is!).Reliabilityestimationusingstatisticaltestingismoreaccuratecomparedtothoseofothermethods such as ROCOF, POFOD etc. But it is not easy to perform statistical testing properly.There is no simple and repeatable way of defining operation profiles. Also it is very muchcumbersome to generate test cases for statistical testing because the number of test cases withwhichthe system is to betested should bestatisticallysignificant. SOFTWAREMAINTENANCEPROCESSMODELS Two broad categories of process models for software maintenance can be proposed. The firstmodel is preferred for projects involving small reworks where the code is changed directly andthe changes are reflected in the relevant documents later. This maintenance process is graphicallypresented in fig. 25.1. In this approach, the project starts by gathering the requirements forchanges. The requirements are next analyzed to formulate the strategies to be adopted for codechange. At this stage, the association of at least a few members of the original development teamgoes a long way in reducing the cycle team, especially for projects involving unstructured andinadequately documented code. The availability of a working old system to the maintenanceengineers at the maintenance site greatly facilitates the task of the maintenance team 115 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) as they get agood insight into the working of the old system and also can compare the working of theirmodified systemwiththe oldsystem.Also,debugging of thereengineeredsystembecomeseasier as the programtraces of boththe systems canbecompared tolocalizethe bugs. Fig.25.1:Maintenanceprocessmodel1 116 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) Thesecondprocessmodelforsoftwaremaintenanceispreferredforprojectswheretheamountof rework required issignificant.Thisapproachcan be represented by a reverse engineeringcycle followed by a forward engineering cycle. Such an approach is also known as softwarereengineering. This process model is depicted in fig. 25.2. The reverse engineering cycle isrequiredforlegacyproducts.Duringthereverseengineering,theoldcodeisanalyzed(abstracted) to extract the module specifications. The module specifications are then analyzed toproduce the design. The design is analyzed (abstracted) to produce the original requirementsspecification. The change requests are then applied to this requirements specification to arrive atthe new requirements specification. At the design, module specification, and coding a substantialreuse is made from the reverse engineered products. An important advantage of this approach isthat it produces a more structured design compared to what the original product had, producesgood documentation, and very often results in increased efficiency. The efficiency improvementsare brought about by a more efficient design. However, this approach is more costly than the firstapproach. An empirical study indicates that process 1 is preferable when the amount of rework isno more than 15%. Besides the amount of rework, several other factors might affect the decisionregardingusingprocessmodel 1 over process model 2:  Reengineeringmightbe preferable forproductswhichexhibitahighfailurerate.  Reengineering might also be preferable for legacy products having poor designandcodestructure. 117 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) Fig.25.2:Maintenanceprocessmodel2 SoftwareReengineering Software reengineering isa combination of two consecutive processesi.e. software reverseengineeringand softwareforward engineeringasshown inthe fig.25.2. Estimationofapproximatemaintenancecost It is well known that maintenance efforts require about 60% of the total life cycle cost for atypical software product. However, maintenance costs vary widely from one application domainto another. For embedded systems, the maintenance cost can be as much as 2 to 4 times thedevelopmentcost. Boehm[1981]proposedaformulaforestimatingmaintenancecostsaspartofhisCOCOMOcost estimation model.Boehm’smaintenancecost estimation is made in termsofaquantitycalled the Annual Change Traffic (ACT). Boehm defined ACT as the fraction of a softwareproduct’s source instructions which undergo change during a typical year either through additionordeletion. ACT= KLOCadded+KLOC deleted KLOCtotal where,KLOCaddedisthetotalkilolines ofsourcecodeaddedduringmaintenance. KLOCdeletedisthe total kilolines ofsourcecodedeletedduringmaintenance. Thus, the code that is changed, should be counted in both the code added and the code deleted.The annual change traffic (ACT) is multiplied with the total development cost to arrive at themaintenance cost: maintenance cost=ACT ×developmentcost. Most maintenance cost estimation models, however, yield only approximate results because theydo not take into account several factors such as experience level of the engineers, and familiarityofthe engineerswith the product, hardwarerequirements, softwarecomplexity, etc. 118 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) 26 SOFTWARERELIABILITYANDQUALITYMANAGEMENT Repeatablevs.non-repeatablesoftwaredevelopmentorganization A repeatable softwaredevelopmentorganizationisone inwhichthesoftware developmentprocessisperson-independent.Inanonrepeatablesoftwaredevelopmentorganization,asoftwaredevelopmentprojectbecomessuccessfulpri marilyduetotheinitiative,effort,brilliance, or enthusiasm displayed by certain individuals. Thus, in a non-repeatable softwaredevelopment organization, the chances of successful completion of a software projectis to agreatextent depends on theteam members. SoftwareReliability Reliabilityofasoftwareproductessentiallydenotesitstrustworthinessordependability.Alternatively, reliability of a software product can also be defined as the probability of theproductworking“correctly”over a given period of time. Itisobviousthatasoftwareproducthavingalargenumberofdefectsisunreliable.Itisalsoclear that the reliability of a system improves, if the number of defects in it is reduced. However,there is no simple relationship between the observed system reliability and the number of latentdefects in the system. For example, removing errors from parts of a software which are rarelyexecutedmakeslittledifferencetotheperceivedreliabilityoftheproduct.Ithasbeenexperimentall y observed by analyzing the behavior of a large number of programs that 90% ofthe execution time of a typical program is spent in executing only 10% of the instructions in theprogram. These most used 10% instructions are often called the core of the program. The rest90% of the program statements are called non-core and are executed only for 10% of the totalexecution time.It therefore may not be very surprising to note that removing 60% productdefects from the least used parts of a system would typically lead to only 3% improvement to theproduct reliability. It 119 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) is clear that the quantity by which the overall reliability of a programimproves due to the correction of a single error depends on how frequently the correspondinginstructionisexecuted. Thus,reliability of a productdependsnotonly on the number of latenterrorsbutalsoon theexact location of the errors. Apart from this, reliability also depends upon how the product isused, i.e. on its execution profile. If it is selected input data to the system such that only the“correctly” implemented functions are executed, none of the errors will be exposed and theperceived reliability of the product will be high. On the other hand, if the input data is selectedsuch that only those functions which contain errors are invoked, the perceived reliability of thesystemwill be verylow. Reasonsforsoftwarereliabilitybeingdifficulttomeasure Thereasonswhysoftwarereliabilityisdifficulttomeasurecanbesummarizedasfollows:  Thereliabilityimprovementduetofixingasinglebugdependsonwherethebugislocatedin the code.  Theperceived reliabilityof asoftwareproduct ishighlyobserver-dependent.  Thereliabilityofaproduct keepschanging as errors aredetected and fixed.  Hardwarereliabilityvs. softwarereliabilitydiffers. Reliability behavior for hardware and software are very different. For example, hardware failuresare inherently differentfromsoftware failures.Mosthardware failuresare due tocomponentwear and tear. A logic gate may be stuck at 1 or 0, or a resistor might short circuit. To fixhardware faults, one has to either replace or repair the failed part. On the other hand, a softwareproduct would continue to fail until the error is tracked down and either the design or the code ischanged. For this reason, when a hardware is repaired its reliability is maintained at the level thatexisted before the failure occurred; whereas when a software failure is repaired, the reliabilitymay either increase or decrease (reliability may decrease if a bug introduces new errors). To putthis fact in a different perspective, hardware reliability study is concerned with stability (forexample, inter-failure times remain constant). On the other hand, software reliability study aimsat reliability growth (i.e. inter-failure times increase). The change of failure rate over the productlifetime for a typical hardware and a software product are sketched in fig. 26.1. For hardwareproducts,itcanbeobservedthatfailurerateishighinitiallybutdecreasesasthefaultycomponent s are identified and removed. The system then enters its useful life. After some time(called product life time) the components wear out, and the failure rate increases. This gives theplot of hardware reliability over time its characteristics “bath tub” shape. On the other hand, forsoftwarethefailurerateisatit’shighestduringintegrationandtest.Asthesystemistested,more and more errors are identified and removed resulting in reduced failure rate. This errorremovalcontinuesataslowerpaceduringtheusefullifeoftheproduct.Asthesoftwarebecomesobso 120 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) letenoerrorcorrections occursand thefailurerate remains unchanged. 121 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) (a) Hardwareproduct (b) Softwareproduct Fig.26.1: Changeinfailurerateofaproduct ReliabilityMetrics The reliability requirements for different categories of software products may be different. Forthis reason, it is necessary that the level of reliability required for a software product should bespecified in the SRS (software requirements specification) document. In order to be able to dothis, some metrics are needed to quantitatively express the reliability of a software product. Agood reliability measure should be observer-dependent, so that different people can agree on thedegree of reliability a system has. For example, there are precise techniques for measuringperformance, which would result in obtaining the same performance value irrespective 122 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) of who iscarrying out the performance measurement. However, in practice, it is very difficult to formulateaprecisereliabilitymeasurement technique. Thenext basecaseis to have measures that correlate 123 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) with reliability. There are six reliability metrics which can be used to quantify the reliability ofsoftwareproducts.  Rateofoccurrenceoffailure(ROCOF)-ROCOFmeasuresthefrequencyofoccurrence of unexpected behavior (i.e. failures). ROCOF measure of a software productcan be obtained by observing the behavior of a software product in operation over aspecifiedtimeintervalandthenrecordingthetotalnumberoffailuresoccurringduringtheinterv al.  MeanTimeToFailure(MTTF)-MTTFistheaveragetimebetweentwosuccessive failures, observed over a large number of failures. To measure MTTF, we can record thefailuredatafornfailures.Letthefailuresoccuratthetimeinstantst,t,…,t.Then, 1 2 MTTFcan becalculatedas n It is important to note that only run time is considered in the time measurements, i.e. thetime for which the system is down to fix the error, the boot time, etc are not taken intoaccountin the timemeasurements and theclockis stopped at thesetimes.  Mean Time To Repair (MTTR) - Once failure occurs, sometime is required to fix theerror. MTTR measures the average time it takes to track the errors causing the failure andto fixthem.  Mean Time Between Failure (MTBR) - MTTF and MTTR can be combined to get theMTBR metric: MTBF = MTTF + MTTR. Thus, MTBF of 300 hours indicates that once afailureoccurs,thenextfailureisexpectedafter300hours.Inthiscase,timemeasurementsarereal time and nottheexecution time as inMTTF.  Probability of Failure on Demand (POFOD) - Unlike the other metrics discussed, thismetricdoesnotexplicitlyinvolvetimemeasurements.POFODmeasuresthelikelihoodof the system failing when a service request is made. For example, a POFOD of 0.001wouldmean that 1 outofevery1000 servicerequestswould result in afailure.  Availability- Availability of a system is a measure of how likely shall the system beavailableforuseoveragivenperiodoftime.Thismetricnotonlyconsidersthenumberof failures occurring during a time interval, but also takes into account the repair time(down time) of a system when a failure occurs. This metric is important for systems suchas telecommunication systems, and operating systems, which are supposed to be neverdown and where repair and restart time are significant and loss of service during that timeis important. 124 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) Classificationofsoftwarefailures Apossibleclassificationoffailuresofsoftwareproductsintofivedifferenttypesisasfollows:  Transient-Transientfailuresoccuronlyforcertaininputvalueswhileinvokingafunctionofthe system.  Permanent-Permanentfailuresoccurforallinputvalueswhileinvokingafunctionofthesystem.  RecoverableWhenrecoverablefailuresoccur,thesystemrecoverswithorwithoutoperatorintervention.  Unrecoverable-Inunrecoverable failures,thesystemmayneedtobe restarted.  Cosmetic- These classes of failures cause only minor irritations, and do not lead toincorrect results. An example of a cosmetic failure is the case where the mouse button hasto be clicked twice instead of once to invoke a given function through the graphical userinterface. 125 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) MODULE4 SOFTWAREQUALITY Traditionally, a quality product is defined in terms of its fitness of purpose. That is, a qualityproduct does exactly what the users want it to do. For software products, fitness of purpose isusually interpreted in terms of satisfaction of the requirements laid down in the SRS document.Although “fitness of purpose” is a satisfactory definition of quality for many products such as acar, a table fan, a grinding machine, etc. – for software products, “fitness of purpose” is not awholly satisfactory definition of quality. To give an example, consider a software product that isfunctionally correct. That is, it performs all functions as specified in the SRS document. But, hasanalmostunusableuserinterface.Eventhoughitmaybefunctionallycorrect,wecannotconsider it to be a quality product. Another example may be that of a product which doeseverything that the users want but has an almost incomprehensible and unmaintainable code.Therefore, the traditional concept of quality as “fitness of purpose” for software products is notwhollysatisfactory. The modern view of a quality associates with a software product several quality factors such asthefollowing:  Portability: A software product is said to be portable, if it can be easily made to work indifferentoperatingsystemenvironments,indifferentmachines,withothersoftwareproducts, etc.  Usability: A software product has good usability, if different categories of users (i.e. bothexpertand noviceusers)can easilyinvoke thefunctions of theproduct.  Reusability: A software product has good reusability, if different modules of the productcan easilybereusedto develop newproducts. 126 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book)  Correctness: A software product is correct, if different requirements as specified in theSRSdocument havebeen correctlyimplemented.  Maintainability: A software product is maintainable, if errors can be easily corrected asand when they show up, new functions can be easily added to the product, and thefunctionalitiesof theproduct can beeasilymodified, etc. SoftwareQualityManagementSystem A quality management system (often referred to as quality system) is the principal methodologyusedbyorganizations to ensurethatthe products theydevelop havethedesired quality. Qualitysystemconsistsofthefollowing: ManagerialStructureandIndividualResponsibilities-Aqualitysystemisactuallytheresponsibility of the organization as a whole. However, every organization has a separate qualitydepartment to perform several quality system activities. The quality system of an organizationshould have support of the top management. Without support for the quality system at a highlevelin acompany, few members ofstaff willtakethe qualitysystem seriously. QualitySystemActivities-Thequalitysystem activitiesencompass thefollowing: - auditingofprojects - reviewofthequalitysystem - developmentofstandards,procedures,and guidelines,etc. - productionofreportsforthetopmanagementsummarizingtheeffectivenessofthequalitys ystem in the organization. EvolutionofQualityManagementSystem Quality systems have rapidly evolved over the last five decades. Prior to World War II, the usualmethod to produce quality products was to inspect the finished products to eliminate defectiveproducts.Sincethattime,qualitysystemsoforganizationshaveundergonethroughfourstages of evolution as shown in the fig. 28.1. The initial product inspection method gave way to qualitycontrol(QC).Qualitycontrolfocusesnotonlyondetectingthedefectiveproductsandeliminating them but also on determining the causes behind the defects. Thus, quality controlaims at 127 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) correcting the causes of errors and not just rejecting the products. The next breakthroughinqualitysystems was thedevelopment of qualityassuranceprinciples. Thebasicpremiseofmodernqualityassuranceisthatifanorganization’sprocessesaregoodand are followed rigorously, then the products are bound to be of good quality. The modernquality paradigm includes guidance for recognizing, defining, analyzing, and improving theproduction process. Total quality management (TQM) advocates that the process followed by anorganization must be continuously improved through process measurements. TQM goes a stepfurther than quality assurance and aims at continuous process improvement. TQM goes beyonddocumenting processes to optimizing them through redesign. A term related to TQM is BusinessProcess Reengineering (BPR). BPR aims at reengineering the way business is carried out in anorganization. From the above discussion it can be stated that over the years the quality paradigmhasshifted from product assuranceto processassurance(as shownin fig. 28.1). 128 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) Fig.28.1:Evolution ofqualitysystemand correspondingshift inthequalityparadigm ISO9000certification ISO(InternationalStandardsOrganization)isaconsortiumof63countriesestablishedtoformulate and foster standardization. ISO published its 9000 series of standards in 1987. ISOcertificationservesasareferenceforcontractbetweenindependentparties.TheISO9000standard specifies the guidelines for maintaining a quality system. We have already seen that thequalitysystemofanorganizationappliestoallactivitiesrelatedtoitsproductorservice.TheISOstanda rdmainlyaddressesoperationalaspectsandorganizationalaspectssuchasresponsibilities, reporting, etc. In a nutshell, ISO 9000 specifies a set of guidelines for repeatableand high quality product development. It is important to realize that ISO 9000 standard is a set ofguidelines for theproduction process and is not directlyconcerned about theproduct itself. TypesofISO9000qualitystandards ISO 9000 is a series of three standards: ISO 9001, ISO 9002, and ISO 9003. The ISO 9000 seriesofstandardsisbasedonthepremisethatifaproperprocessisfollowedforproduction,thengood quality products are bound to follow automatically. The types of industries to which thedifferentISO standardsapplyareas follows. ISO 9001 applies to the organizations engaged in design, development, production, and servicingofgoods.This isthestandardthat isapplicable tomost softwaredevelopment organizations. ISO 9002 applies to those organizations which do not design products but are only involved inproduction. Examples of these category industries include steel and car manufacturing industriesthatbuytheproductandplantdesignsfromexternalsourcesandareinvolvedinonlymanufactu ring those products. Therefore, ISO 9002 is not applicable to software developmentorganizations. ISO9003appliestoorganizationsthatareinvolvedonly ininstallationandtestingoftheproducts. Softwareproductsvs.otherproducts Therearemainlytwodifferences between softwareproducts andanyothertypeof products.  Software is intangible in nature and therefore difficult to control. It is very difficult tocontrol and manage anything that is not seen. In contrast, any other industries such as 129 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) carmanufacturing industries where one can see a product being developed through variousstagessuchasfittingengine,fittingdoors,etc.Therefore,itiseasytoaccuratelydetermine how much work has been completed and to estimate how much more time willittake.  During software development, the only raw material consumed is data. In contrast, largequantitiesof raw materials areconsumedduringthedevelopment ofanyotherproduct. NeedforobtainingISO9000certification ThereisamadscrambleamongsoftwaredevelopmentorganizationsforobtainingISOcertification due to the benefits it offers. Some benefits that can be acquired to organizations byobtainingISO certification areasfollows:  Confidence of customers in an organization increaseswhenorganization qualifiesforISOcertification.Thisisespeciallytrueintheinternationalmarket.Infact,manyorga nizationsawardinginternationalsoftwaredevelopmentcontractsinsistthatthedevelopment organization have ISO 9000 certification. For this reason, it is vital forsoftwareorganizationsinvolvedin softwareexportto obtainISO9000certification.  ISO 9000 requires a well-documented software production process to be in place. A welldocumented software production process contributes to repeatable and higher quality ofthedeveloped software.  ISO9000makesthedevelopmentprocessfocused,efficient, andcost-effective.  ISO 9000 certification points out the weak points of an organization and recommendsremedialaction.  ISO 9000 sets the basic framework for the development of an optimal process and TotalQualityManagement (TQM). summaryofISO9001certification AsummaryofthemainrequirementsofISO9001astheyrelateofsoftwaredevelopmentisasfollows.Section numbers inbrackets correspond to thosein thestandard itself: ManagementResponsibility(4.1)  Themanagementmust haveaneffectivequalitypolicy.  The responsibility and authority of all those whose work affects quality must be definedanddocumented.  Amanagementrepresentative,independentofthedevelopmentprocess,mustberesponsible for the quality system. This requirement probably has been put down so thattheperson 130 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) responsibleforthequalitysystem canwork in an unbiasedmanner.  Theeffectiveness of thequalitysystem must be periodicallyreviewed byaudits. QualitySystem(4.2) Aqualitysystemmust bemaintainedand documented. ContractReviews(4.3) Beforeenteringintoacontract,anorganizationmustreviewthecontracttoensurethatitisunderstood,and that the organization has thenecessarycapabilityfor carryingout its obligations. DesignControl(4.4)  Thedesignprocessmustbeproperlycontrolled,thisincludescontrollingcodingalso.Thisrequir ementmeansthat a goodconfiguration controlsystemmust beinplace.  Designinputsmustbeverifiedasadequate.  Designmustbeverified.  Designoutputmustbeof requiredquality.  Design changesmustbecontrolled. DocumentControl(4.5)  Theremustbeproper proceduresfordocument approval,issueandremoval.  Documentchangesmustbecontrolled.Thus,useofsomeconfigurationmanagementtoolsis necessary. Purchasing(4.6) Purchasingmaterial,includingbought-insoftwaremustbecheckedforconformingtorequirements. PurchaserSuppliedProduct(4.7) Materialsuppliedbyapurchaser,forexample,clientprovidedsoftwaremustbeproperlymanagedandchecked. 131 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) ProductIdentification (4.8) The product must be identifiable at all stages of the process. In software terms this meansconfigurationmanagement. ProcessControl(4.9)  Thedevelopment must beproperlymanaged.  Qualityrequirement must be identifiedin a qualityplan. InspectionandTesting(4.10) In software terms this requires effective testing i.e., unit testing, integration testing and systemtesting.Test records must be maintained. Inspection,MeasuringandTestEquipment(4.11) If integration, measuring, and test equipments are used, they must be properly maintained andcalibrated. InspectionandTestStatus(4.12) Thestatusofanitemmustbeidentified.Insoftwaretermsthisimpliesconfigurationmanagementandrele asecontrol. ControlofNonconformingProduct(4.13) In software terms, this means keeping untested or faulty software out of the released product, orotherplaceswhetherit might causedamage. CorrectiveAction (4.14) This requirement is both about correcting errors when found, and also investigating why theerrors occurred and improving the process to prevent occurrences. If an error occurs despite thequalitysystem, thesystem needs improvement. Handling,(4.15) This clause deals with thestorage, packing, and deliveryof thesoftwareproduct. Qualityrecords(4.16) Recording the steps taken to control the quality of the process is essential in order to be able toconfirm that theyhaveactuallytaken place. QualityAudits(4.17) Auditsof thequalitysystemmust be carried out toensurethat it is effective. 132 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) Training(4.18) Trainingneedsmust beidentified andmet. SalientfeaturesofISO9001certification ThesalientfeaturesofISO9001areasfollows:  All documents concerned with the development of a software product should be properlymanaged,authorized,andcontrolled.Thisrequiresaconfigurationmanagementsyste mtobe in place.  Properplansshouldbepreparedandthenprogressagainsttheseplansshouldbemonitored.  Important documents should be independently checked and reviewed for effectivenessandcorrectness.  Theproductshouldbetestedagainst specification.  Several organizational aspects should be addressed e.g., management reporting of thequalityteam. ShortcomingsofISO9000 certification Even though ISO 9000 aims at setting up an effective quality system in an organization, it suffersfrom several shortcomings. Some of these shortcomings of the ISO 9000 certification process arethefollowing:  ISO 9000 requires a software production process to be adhered to but does not guaranteethe process to be of high quality. It also does not give any guideline for defining anappropriateprocess.  ISO 9000 certification process is not fool-proof and no international accreditation agencyexists. Therefore it is likely that variations in the norms of awarding certificates can existamongthe different accreditationagenciesand alsoamongtheregistrars.  Organizations getting ISO 9000 certification often tend to downplay domain expertise.These organizations start to believe that since a good process is in place, any engineer isas effective as any other engineer in doing any particular activity relating to softwaredevelopment.However,many areasofsoftwaredevelopmentare sospecializedthatspecialexpertiseandexperienceintheseareas(domainexpertise)isrequired.I nmanufacturing industry there is a clear link between process quality and product 133 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) quality.Once a process is calibrated, it can be run again and again producing quality goods. Incontrast, software development is a creative process and individual skills and experienceareimportant.  ISO 9000 does not automatically lead to continuous process improvement, i.e. does notautomaticallylead to TQM. SEICAPABILITYMATURITY MODEL SEI Capability Maturity Model (SEI CMM) helped organizations to improve the quality of thesoftware they developandthereforeadoptionof SEI CMMmodelhassignificantbusinessbenefits. SEICMMcanbeusedtwoways:capabilityevaluationandsoftwareprocessassessment.Capability evaluation and software process assessment differ in motivation, objective, and thefinal use of the result. Capability evaluation provides a way to assess the software processcapability of an organization. The results of capability evaluation indicates the likely contractorperformance if the contractor is awarded a work. Therefore, the results of software processcapability assessment can be used to select a contractor. On the other hand, software processassessment is used by an organization with the objective to improve its process capability. Thus,thistypeof assessment is forpurelyinternal use. SEI CMM classifies software development industries into the following five maturity levels. Thedifferent levels of SEI CMM have been designed so that it is easy for an organization to slowlybuildits qualitysystem starting from scratch. Level 1: Initial - A software development organization at this level is characterized by ad hocactivities.Veryfewornoprocessesaredefinedandfollowed.Sincesoftwareproductionprocessesare notdefined,differentengineersfollowtheirownprocessandasaresultdevelopment efforts become chaotic. Therefore, it is also called chaotic level. The success ofprojects depends on individual efforts and heroics. When engineers leave, the successors havegreat difficulty in understanding the process followed and the work completed. Since formalproject management practices are not followed, under time pressure short cuts are tried outleadingto low quality. 134 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) Level 2: Repeatable - At this level, the basic project management practices such as tracking costand schedule are established. Size and cost estimation techniques like function point analysis,COCOMO, etc. are used. The necessary process discipline is in place to repeat earlier success onprojects with similar applications. Please remember that opportunity to repeat a process existsonlywhenacompanyproduces a familyof products. Level 3: Defined - At this level the processes for both management and development activitiesare defined and documented. There is a common organization-wide understanding of activities,roles, and responsibilities. The processes thoughdefined, the process and product qualities arenotmeasured.ISO 9000aims at achievingthis level. Level 4: Managed - At this level, the focus is on software metrics. Two types of metrics arecollected. Product metrics measure the characteristics of the product being developed, such as itssize, reliability, time complexity, understandability, etc. Process metrics reflect the effectivenessof the process being used, such as average defect correction time, productivity, average numberof defectsfound per hour inspection,averagenumber offailures detected during testing perLOC, etc. Quantitative quality goals are set for the products. The software process and productquality are measured and quantitative quality requirements for the product are met. Various toolslike Pareto charts, fishbone diagrams, etc. are used to measure the product and process quality.The process metrics are used to check if a project performed satisfactorily. Thus, the results ofprocessmeasurementsareusedto evaluateprojectperformanceratherthanimprovetheprocess. Level 5: Optimizing - At this stage, process and product metrics are collected. Process andproduct measurement data are analyzed for continuous process improvement. For example, iffrom an analysis of the process measurement results, it was found that the code reviews were notvery effective and a large number of errors were detected only during the unit testing, then theprocess may be fine-tuned to make the review more effective. Also, the lessons learned fromspecific projects are incorporated in to the process. Continuous process improvement is achievedboth by carefully analyzing the quantitative feedback from the process measurements and alsofrom application of innovative ideas and technologies. Such an organization identifies the bestsoftware engineering practicesand innovationswhich may be tools,methods, or processes.Thesebest practicesaretransferredthroughout theorganization. Keyprocessareas(KPA)of asoftwareorganization Except for SEI CMM level 1, each maturity level is characterized by several Key Process Areas(KPAs) that includes the areas an organization should focus to improve its software processtothe next level. The focus of each level and the corresponding key process areas are 135 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) shown in thefig.29.1. 136 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) CMMLevel 1.Initial 2.Repeatable 3.Defined Focus Competentpeople Projectmanagement Definitionofprocesses KeyProcessAres Softwareprojectplanning Softwareconfigurationmanagement Processdefinition Training programPeerrevi ews 4.Managed 5.Optimizing Product and process quality Continuousprocess improvement Quantitativeprocessmetrics Softwarequalitymanagement Defectprevention Process change managementTechnologychange management Fig.29.1: Thefocus of eachSEICMM levelandthe correspondingkeyprocess areas SEICMMprovidesalistofkeyareasonwhichtofocustotakeanorganizationfromonelevelof maturity to the next. Thus, it provides a way for gradual quality improvement over severalstages.Eachstage hasbeen carefully designedsuch that one stage enhances the capabilityalready built up. For example, it considers that trying to implement a defined process (SEI CMMlevel3)beforearepeatableprocess(SEI CMMlevel2)wouldbecounterproductiveasitbecomes difficult to follow the defined process due to schedule and budget pressures. ISO 9000certificationvs. SEI/CMM For quality appraisal of a software development organization, the characteristics of ISO 9000certificationand theSEICMM differin some respects.The differencesareas follows:  ISO9000isawardedbyaninternationalstandardsbody.Therefore,ISO9000certification can be quoted by an organization in official documents, communication withexternal parties, and the tender quotations. However, SEI CMM assessment is purely forinternaluse.  SEI CMM was developed specifically for software industry and therefore addresses manyissueswhich arespecificto softwareindustryalone.  SEI CMM goes beyond quality assurance and prepares an organization to ultimatelyachieve Total Quality Management (TQM). In fact, ISO 9001 aims at level 3 of SEICMM model.  SEI CMM model provides a list of key process areas (KPAs) on which an organization atany maturity level needs to concentrate to take it from one maturity level to the next.Thus, it provides awayforachievinggradual qualityimprovement. 137 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) 138 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) Applicabilityof SEICMMtoorganizations Highly systematic and measured approach to software development suits large organizationsdealing with negotiated software, safety-critical software, etc. For those large organizations, SEICMMmodelisperfectlyapplicable.ButsmallorganizationstypicallyhandleapplicationssuchasInt ernet,e-commerce,andarewithoutanestablishedproductrange,revenuebase,andexperience on past projects, etc. For such organizations, a CMM-based appraisal is probablyexcessive. These organizations need to operate more efficiently at the lower levels of maturity.Forexample,theyneedtopracticeeffectiveprojectmanagement,reviews,configurationmana gement,etc. PersonalSoftwareProcess Personal Software Process (PSP) is a scaled down version of the industrial software process. PSPis suitable for individual use. It is important to note that SEI CMM doesnot tell softwaredevelopers how to analyze, design, code, test, or document software products, but assumes thatengineers use effective personal practices. PSP recognizes that the process for individual use isdifferentfrom that necessaryforateam. Thequalityandproductivityofanengineeristoagreatextentdependentonhisprocess.PSPisaframework thathelpsengineerstomeasureandimprovetheway they work.Ithelpsindeveloping personal skills and methods by estimating and planning, by showing how to trackperformance againstplans, andprovides adefinedprocesswhichcanbetunedbyindividuals. TimemeasurementPSPadvocatesthatengineersshouldrackthewaytheyspendtime.Because,boringactivitiesseemlongert hanactualandinterestingactivitiesseemshort.Therefore, the actual time spent on a task should be measured with the help of a stop-clock to getan objective picture of the time spent. For example, he may stop the clock when attending atelephone call, taking a coffee break etc. An engineer should measure the time he spends fordesigning,writingcode, testing, etc. PSP Planning- Individuals must plan their project. They must estimate the maximum, minimum,andtheaverageLOCrequiredfortheproduct.Theyshouldusetheirproductivityinminutes/L OC to calculate the maximum, minimum, and the average development time. Theymustrecord the plan dataina project plan summary. The PSP is schematically shown in fig. 29.2. While carrying out the different phases, they mustrecord the log data using time measurement. During post-mortem, they can compare the log datawith their project plan to achieve better planning in the future projects, to improve their process,etc. 139 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) Fig.29.2:SchematicrepresentationofPSP ThePSPlevelsaresummarizedinfig.29.3. 140 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) Fig.29.3:LevelsofPSP PSP2 introduces defect management via the use of checklists for code and design reviews. Thechecklistsaredevelopedfrom gatheringandanalyzingdefect dataearlierprojects. SixSigma The purpose of Six Sigma is to improve processes to do things better, faster, and at lower cost. Itcan be used to improve every facet of business, from production, to human resources, to orderentry, to technical support. Six Sigma can be used for any activity that is concerned with cost,timeliness,and qualityofresults. Therefore,it is applicable to virtuallyeveryindustry. Six Sigma at many organizations simply means striving for near perfection. Six Sigma is adisciplined, data-driven approach to eliminate defects in any process – from manufacturing totransactionaland productto service. The statistical representation of Six Sigma describes quantitatively how a process is performing.ToachieveSixSigma,aprocessmustnotproducemorethan3.4defectspermillionopportuni ties. A Six Sigma defect is defined as any system behavior that is not as per customerspecifications. Total number of Six Sigma opportunities is then the total number of chances for adefect.Process sigma can easilybecalculated usingaSixSigmacalculator. ThefundamentalobjectiveoftheSixSigmamethodologyistheimplementationofameasurementbasedstrategythatfocusesonprocessimprovementandvariationreductionthroughtheapplicationofSix Sigmaimprovementprojects.ThisisaccomplishedthroughtheuseoftwoSix Sigmasubmethodologies:DMAICandDMADV.TheSixSigmaDMAICprocess (define, measure, analyze, improve, control) is an improvement system for existingprocesses failing below specification and looking for incremental improvement. The Six SigmaDMADV process (define, measure, analyze, design, verify) is an improvement system used todevelop new processes or products at Six Sigma quality levels. It can also be employed if acurrent process requires more than just incremental improvement. Both Six Sigma processes areexecuted by Six Sigma Green Belts and Six Sigma Black Belts, and are overseen by Six SigmaMasterBlackBelts. Many frameworks exist for implementing the Six Sigma methodology. Six Sigma Software Engineering Keerthana P, Manasa KN, Ganga D Bengal 141 ISBN : 978-81-963532-2-3 (E-Book) Consultantsall over the world have also developed proprietary methodologies for implementing Six Sigmaquality,basedon thesimilarchangemanagementphilosophies andapplicationsof tools. 142 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) SOFTWAREPROJECTPLANNING Project Planning and Project Estimation TechniquesResponsibilitiesofasoftwareproject manager Software project managers take the overall responsibility of steering a project to success. It isvery difficulttoobjectively describe the jobresponsibilitiesofa projectmanager. The jobresponsibility of a project manager ranges from invisible activities like building up team moraleto highly visible customer presentations. Most managers take responsibility for project proposalwriting, project cost estimation, scheduling, project staffing, software process tailoring, projectmonitoring and control, software configuration management, risk management, interfacing withclients, managerial report writing and presentations, etc. These activities are certainly numerous,varied and difficult to enumerate, but these activities can be broadly classified into projectplanning,andprojectmonitoringandcontrolactivities.Theprojectplanningactivityisundertake nbeforethedevelopmentstartstoplantheactivitiestobeundertakenduringdevelopment. The project monitoring and control activities are undertaken once the developmentactivities start with the aim of ensuring that the development proceeds as per plan and changingtheplan whenever required to copeup withthe situation. Skillsnecessaryforsoftwareprojectmanagement A theoretical knowledge of different project management techniques is certainly necessary tobecomeasuccessfulprojectmanager.However,effectivesoftwareprojectmanagementfrequently calls for good qualitative judgment and decision taking capabilities. In addition tohavingagoodgraspofthelatestsoftwareprojectmanagementtechniquessuchascostestimation,riskm anagement,configurationmanagement,projectmanagersneedgoodcommunication skills and the ability get work done. However, some skills such as tracking andcontrolling the progress of the project, customer interaction, managerial presentations, and teambuildingarelargelyacquiredthroughexperience.Nonetheless,theimportanceofsoundknowledge of theprevalent project managementtechniques cannot be overemphasized. ProjectPlanning Once a project is found to be feasible, software project managers undertake project planning.Projectplanningisundertakenandcompletedevenbeforeanydevelopmentactivitystarts.Proj 143 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) ectplanning consists of thefollowingessential activities:  Estimatingthefollowingattributesofthe project:  Project size: What will be problem complexity in terms of the effort and timerequiredto develop theproduct?  Cost:Howmuch isit goingtocost todevelop the project?  Duration:How longisit goingto taketo completedevelopment?  Effort:Howmucheffort wouldberequired? Theeffectivenessofthesubsequentplanningactivitiesisbasedontheaccuracyoftheseestimatio ns.  Schedulingmanpowerandotherresources.  Stafforganizationandstaffingplans.  Riskidentification,analysis,andabatementplanning  Miscellaneousplanssuch asqualityassuranceplan,configurationmanagementplan,etc. Precedenceorderingamongprojectplanningactivities Differentprojectrelatedestimatesdonebyaprojectmanagerhavealreadybeendiscussed.Fig. 30.1 shows the order inwhich important projectplanning activitiesmay be undertaken.Fromfig. 30.1 it can be easily observed that size estimation is the first activity. It is also the mostfundamentalparameterbasedonwhichallotherplanningactivitiesarecarriedout.Otherestimation ssuchasestimationofeffort,cost,resource,andprojectdurationarealsoveryimportantcomponents of project planning. 144 Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) Fig.30.1: Precedenceorderingamongplanningactivities SlidingWindowPlanning Project planning requires utmost care and attention since commitment to unrealistic time andresourceestimatesresultinscheduleslippage.Scheduledelayscancausecustomerdissatisfactionan dadverselyaffectteammorale.Itcanevencauseprojectfailure.However, project planning is a very challenging activity. Especially for large projects, it is very muchdifficult to make accurate plans. A part of this difficulty is due to the fact that the properparameters, scope of the project, project staff, etc. may change during the span of the project. Inorder to overcome this problem, sometimes projectmanagers undertake project planning instages.Planningaprojectoveranumberofstagesprotectsmanagersfrommakingbigcommitments too early. This technique of staggered planning is known as Sliding WindowPlanning. In the sliding window technique, starting with an initial plan, the project is plannedmore accurately in successive development stages. At the start of a project, project managershave incomplete knowledge about the details of the project. Their information base graduallyimproves as the project progresses through different phases. After the completion of every phase,the project managers can plan each subsequent phase more accurately and with increasing levelsofconfidence. SoftwareProjectManagementPlan(SPMP) Once project planning is complete, project managers document their plans in a Software ProjectManagement Plan (SPMP) document. The SPMP document should discuss a list of differentitems that have been discussed below. This list can be used as a possible organization of theSPMP document. OrganizationoftheSoftwareProjectManagementPlan(SPMP)Document 1. Introduction (a) Objectives (b) MajorFunctions (c) PerformanceIssues (d) ManagementandTechnicalConstraints 2. ProjectEstimates (a) HistoricalDataUsed (b) EstimationTechniquesUsed 145 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) (c) Effort,Resource,Cost,and ProjectDurationEstimates 3. Schedule (a) WorkBreakdownStructure (b) TaskNetworkRepresentation (c) GanttChartRepresentation (d) PERTChartRepresentation 4. ProjectResources (a) People (b) Hardwareand Software (c) SpecialResources 5. StaffOrganization (a) TeamStructure (b) ManagementReporting 6. RiskManagementPlan (a) RiskAnalysis (b) RiskIdentification (c) Risk Estimation (d) RiskAbatementProcedures 7. ProjectTrackingandControlPlan 8. MiscellaneousPlans (a) ProcessTailoring (b) QualityAssurance Plan (c) ConfigurationManagementPlan (d) ValidationandVerification 146 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) (e) SystemTestingPlan (f) Delivery, Installation, andMaintenancePlan METRICSFORSOFTWAREPROJECTSIZEESTIMATION Accurate estimation of the problem size is fundamental to satisfactory estimation of effort, timeduration and cost of a software project. In order to be able to accurately estimate the project size,some important metrics should be defined in terms of which the project size can be expressed.The size of a problem is obviously not the number of bytes that the source code occupies. It isneither the byte size of the executable code. The project size is a measure of the problemcomplexityin terms oftheeffort and time requiredto develop theproduct. Currently two metrics are popularly being used widely to estimate size: lines of code (LOC) andfunction point (FP). The usage of each of these metrics in project size estimation has its ownadvantagesand disadvantages. LinesofCode(LOC) 147 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) LOC is the simplest among all metrics available to estimate project size. This metric is verypopular because it is the simplest to use. Using this metric, the project size is estimated bycounting the number of source instructions in the developed program. Obviously, while countingthenumberofsourceinstructions,linesusedforcommentingthecodeandtheheaderlinesshould beignored. Determining the LOC count at the end of a project is a very simple job. However, accurateestimationoftheLOCcountatthebeginningofaprojectisverydifficult.Inordertoestimatethe LOC count at the beginning of a project, project managers usually divide the problem intomodules, and each module into submodules and so on, until the sizes of the different leaflevelmodules can be approximately predicted. To be able to do this, past experience in developingsimilarproductsishelpful.Byusingtheestimationofthelowestlevelmodules,projectmanag ers arriveat thetotal sizeestimation. Functionpoint(FP) Function point metric was proposed by Albrecht [1983]. This metric overcomes many of theshortcomings of the LOC metric. Since its inception in late 1970s, function point metric has beenslowly gaining popularity. One of the important advantages of using the function point metric isthat it can be used to easily estimate the size of a software product directly from the problemspecification. This is in contrast to the LOC metric, where the size can be accurately determinedonly after the product has fully been developed. The conceptual idea behind the function pointmetric is that the size of a software product is directly dependent on the number of differentfunctions or features it supports. A software product supporting many features would certainly beoflargersizethanaproductwithlessnumberoffeatures.Eachfunctionwheninvokedreads some input data and transforms it to the corresponding output data. For example, the issue bookfeature (as shown in fig. 31.1) of a Library Automation Software takes the name of the book asinput and displays its location and the number of copies available. Thus, a computation of thenumber of input and the output data values to a system gives some indication of the number offunctions supported by the system. Albrecht postulated that in addition to the number of basicfunctions that a software performs, the size is also dependent on the number of files and thenumberof interfaces. 148 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) Fig.31.1: Systemfunction asa mapof inputdatatooutput data Besides using the number ofinput and output data values,function point metric computesthesize of a software product (in units of functions points or FPs) using three other characteristics ofthe product as shown in the following expression. The size of a product in function points (FP)canbeexpressedastheweightedsumofthesefiveproblemcharacteristics.Theweightsassociatedwi ththefivecharacteristicswereproposedempiricallyandvalidatedbytheobservations over many projects. Function point is computed in two steps. The first step is tocomputethe unadjusted function point (UFP). UFP=(Numberofinputs)*4+(Numberofoutputs)*5+(Numberofinquiries)*4+(Numberoffi les)*10+ (Numberofinterfaces)*10 Numberofinputs:Eachdataiteminputbytheuseriscounted.Datainputsshouldbedistinguishedfromus erinquiries.Inquiriesareusercommandssuchasprint-account-balance. Inquiries are counted separately. It must be noted that individual data items input by the user arenot considered in the calculation of the number of inputs, but a group of related inputs 149 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) areconsidered as a single input. For example, while entering the data concerning an employee to anemployee pay roll software; the data items name, age, sex, address, phone number, etc. aretogether considered as a single input. All these data items can be considered to be related, sincetheypertain to asingle employee. Numberofoutputs:Theoutputsconsideredrefertoreportsprinted,screenoutputs,errormessages produced, etc. While outputting the number of outputs the individual data items withinareportarenot considered, but a set ofrelated dataitems is counted as oneinput. Number of inquiries: Number of inquiries is the number of distinct interactive queries whichcan be made by the users. These inquiries are the user commands which require specific actionbythe system. Number of files: Each logical file is counted. A logical file means groups of logically relateddata.Thus, logical files can bedata structures or physicalfiles. Numberofinterfaces:Heretheinterfacesconsideredaretheinterfacesusedtoexchangeinformationwit hother systems.Examplesof suchinterfacesare datafiles ontapes,disks,communicationlinks with othersystems etc. Once the unadjusted function point (UFP) is computed, the technical complexity factor (TCF) iscomputed next. TCF refines the UFP measure by considering fourteen other factors such as hightransaction rates, throughput, and response time requirements, etc. Each of these 14 factors isassigned from 0 (not present or no influence) to 6 (strong influence). The resulting numbers aresummed, yielding the total degree of influence (DI). Now, TCF is computed as (0.65+0.01*DI).AsDIcan varyfrom 0 to 70, TCFcan varyfrom 0.65 to1.35. Finally,FP=UFP*TCF. Shortcomingsoffunctionpoint(FP)metric LOCasameasureofproblemsizehasseveralshortcomings:  LOC gives a numerical value of problem size that can vary widely with individual codingstyle – different programmers lay out their code in different ways. For example, oneprogrammer might write several source instructions on a single line whereas anothermight split a single instruction across several lines. Of course, this problem can be easilyovercome by counting the language tokens in the program rather than the lines of code.However, a more intricate problem arises because the length of a program depends on thechoice of instructions used in writing the program. Therefore, even for the same problem,different programmers might come up with programs having different LOC 150 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) counts. Thissituationdoesnot improveevenif languagetokensarecountedinstead oflines ofcode.  A good problem size measure should consider the overall complexity of the problem andthe effort needed to solve it. That is, it should consider the local effort needed to specify,design, code, test, etc. and not just the coding effort. LOC, however, focuses on thecoding activity alone; it merely computes the number of source lines in the final program.Wehavealreadyseenthatcodingisonlyasmallpartoftheoverallsoftwaredevelopment activities. It is also wrong to argue that the overall product developmenteffort is proportional to the effort required in writing the program code. This is becauseeven though the design might be very complex, the code might be straightforward andviceversa.In suchcases,codesizeis a grosslyimproper indicator of theproblemsize.  LOC measure correlates poorly with the quality and efficiency of the code. Larger codesize does not necessarily imply better quality or higher efficiency. Some programmersproduce lengthy and complicated code as they do not make effective use of the availableinstruction set.In fact,itisvery likely that a poor and sloppily writtenpiece ofcodemighthavelarger numberofsourceinstructions thanapiecethat isneat andefficient.  LOC metric penalizes use of higher-level programming languages, code reuse, etc. Theparadox is that if a programmer consciously uses several library routines, then the LOCcount will be lower. This would show up as smaller program size. Thus, if managers usetheLOCcountasameasureoftheeffortputinthedifferentengineers(thatis,productivity),the ywould bediscouragingcodereuse byengineers.  LOC metric measures the lexical complexity of a program and does not address the moreimportant but subtle issues of logical or structural complexities. Between two programswith equal LOC count, a program having complex logic would require much more effortto develop than a program with very simple logic. To realize why this is so, consider theeffort required to develop a program having multiple nested loop and decision constructswithanother program havingonlysequential control flow.  It is very difficult to accurately estimate LOC in the final product from the problemspecification. The LOC count can be accurately computed only after the code has beenfully developed. Therefore, the LOC metric is little use to the project managers duringprojectplanning,sinceprojectplanning iscarried outevenbefore any developmentactivity has started. This possibly is the biggest shortcoming of the LOC metric from theprojectmanager’sperspective. FeaturePointMetric 151 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) A major shortcoming of the function point measure is that it does not take into account thealgorithmic complexity of a software. That is, the function point metric implicitly assumes thatthe effort required to design and develop any two functionalities of the system is the same. But,we know that this is normally not true, the effort required to develop any two functionalities mayvarywidely.Itonlytakesthenumberoffunctionsthatthesystemsupportsintoconsideration without distinguishing the difficulty level of developing the various functionalities. To overcomethisproblem,an extensionofthefunctionpointmetric calledfeaturepointmetricisproposed. Featurepointmetricincorporatesanextraparameteralgorithmcomplexity.Thisparameterensures that the computed size using the feature point metric reflects the fact that the more is thecomplexity of a function, the greater is the effort required to develop it and therefore its sizeshouldbelargercompared to simplerfunctions. ProjectEstimationTechniques Estimation of various project parameters is a basic project planning activity. The importantprojectparametersthatareestimatedinclude:projectsize,effortrequiredtodevelopthesoftwa re, project duration, and cost. These estimates not only help in quoting the project cost tothe customer, but are also useful in resource planning and scheduling. There are three broadcategoriesof estimation techniques: • Empiricalestimationtechniques • Heuristictechniques • Analyticalestimationtechniques EmpiricalEstimationTechniques Empirical estimation techniques are based on making an educated guess of the projectparameters. While using this technique, prior experience with development of similarproducts is helpful. Althoughempirical estimation techniques are based on commonsense, differentactivities involved inestimation have been formalizedover theyears.Twopopularempiricalestimationtechniquesare:ExpertjudgmenttechniqueandDel phicost estimation. 152 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) ExpertJudgmentTechnique Expert judgment is one of the most widely used estimation techniques. In this approach,an expert makes an educated guess of the problem size after analyzing the problemthoroughly.Usually,theexpertestimatesthecostofthedifferentcomponents(i.e.modu les or subsystems) of the system and then combines them to arrive at the overallestimate. However, this technique is subject to human errors and individual bias. Also, itis possible that the expert may overlook some factors inadvertently. Further, an expertmaking an estimate may not have experience and knowledge of all aspects of a project.For example, he may be conversant with the database and user interface parts but may notbeveryknowledgeableabout thecomputer communication part. A more refined form of expert judgment is the estimation made by group of experts.Estimation by a group of experts minimizes factors such as individual oversight, lack offamiliarity with a particular aspect of a project, personal bias, and the desire to wincontractthroughoverlyoptimisticestimates.However,theestimatemadebyagroupof experts may still exhibit bias on issues where the entire group of experts may be biaseddue to reasons such as political considerations. Also, the decision made by the group maybedominated byoverlyassertivemembers. DelphiCostEstimation Delphi cost estimation approach tries to overcome some of the shortcomings of the expertjudgment approach. Delphi estimation is carried out by a team comprising of a group ofexpertsandacoordinator.Inthisapproach,thecoordinatorprovideseachestimatorwithacopy ofthesoftwarerequirementsspecification(SRS)documentandaformforrecording his cost estimate. Estimators complete their individual estimates anonymouslyand submit to the coordinator. In their estimates, the estimators mention any unusualcharacteristicoftheproductwhichhasinfluencedhisestimation.Thecoordinatorprepar es and distributes the summary of the responses of all the estimators, and includesanyunusualrationalenotedbyanyoftheestimators.Basedonthissummary,theestimato rsre-estimate.Thisprocessisiteratedforseveralrounds.However,nodiscussion among the estimators is allowed during the entire estimation process. The ideabehindthisisthatifanydiscussionisallowedamongtheestimators,thenmanyestimators may easily get influenced by the rationale of an estimator who may be 153 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) moreexperiencedorsenior.Afterthecompletionofseveraliterationsofestimations,thecoordina tortakestheresponsibility ofcompilingtheresultsandpreparingthefinalestimate. HEURISTICTECHNIQUES Heuristic techniques assume that the relationships among the different project parameters can bemodeled using suitable mathematical expressions. Once the basic (independent) parameters areknown,theother(dependent)parameterscanbeeasilydeterminedbysubstitutingthevalueofthebasic parametersinthemathematicalexpression.Differentheuristicestimationmodelscanbedividedinto thefollowingtwoclasses:singlevariablemodel andthemulti variablemodel. Single variable estimation models provide a means to estimate the desired characteristics of aproblem, using some previously estimated basic (independent) characteristic of the softwareproductsuch asits size.A single variable estimation modeltakes the followingform: d EstimatedParameter =c *e 1 1 154 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) In the above expression, e is the characteristic of the software which has already been estimated(independent variable). Estimated Parameter is the dependent parameter to be estimated. Thedependentparametertobeestimatedcouldbeeffort, projectduration,staffsize,etc. canddare 1 constants.Thevaluesoftheconstantscanddareusuallydeterminedusingdatacollectedfrom 1 1 past projects (historical data). The basic COCOMO model is an example of single variable costestimationmodel. Amultivariablecostestimationmodel takesthefollowingform: d d EstimatedResource=c*e + c*e …… 1 155 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) 1 11 1 2 2 22 Wheree, e,…arethebasic(independent) characteristicsofthe software alreadyestimated,and c,c,d,d,…areconstants.Multivariableestimationmodelsareexpectedtogivemore 1 2 1 2 accurate estimates compared to the single variable models, since a project parameter is typicallyinfluencedbyseveralindependentparameters.Theindependentparametersinfluencethedepe ndentparametertodifferentextents.Thisismodeledbytheconstantsc,c,d,d,…. 1 2 1 2 Valuesoftheseconstantsareusuallydeterminedfromhistoricaldata.TheintermediateCOCOMOmode l canbeconsideredto be an exampleof amultivariable estimationmodel. AnalyticalEstimationTechniques Analyticalestimationtechniquesderivetherequiredresultsstarting withbasicassumptionsregarding the project. Thus, unlike empirical and heuristic techniques, analytical techniques dohave scientific basis. Halstead’s software science is an example of an analytical technique.Halstead’ssoftwaresciencecanbeusedtoderivesomeinterestingresultsstartingwithafew simple assumptions. Halstead’s software science is especially useful for estimating softwaremaintenance efforts.Infact, it outperforms bothempirical and heuristic techniques when usedforpredictingsoftwaremaintenanceefforts. Halstead’sSoftwareScience–AnAnalyticalTechnique Halstead’s software science is an analytical technique to measure size, development effort, anddevelopment cost of software products. Halstead used a few primitive program parameters todevelop the expressions for overall program length, potential minimum value, actual volume,effort,and development time. Fora givenprogram,let:  ηbethe numberofuniqueoperators usedinthe program, 1  ηbethe numberofuniqueoperands usedin theprogram,  Nbethe totalnumberofoperators usedin theprogram,  Nbethe totalnumberofoperands usedin theprogram. 2 1 2 LengthandVocabulary ThelengthofaprogramasdefinedbyHalstead,quantifiestotalusageofalloperatorsandoperandsintheprogr am.Thus,lengthN=N+N.Halstead’sdefinitionofthelengthofthe 1 2 programasthetotalnumberofoperatorsandoperandsroughlyagreeswiththeintuitivenotationofthe program lengthasthe total numberoftokens used in theprogram. Theprogramvocabularyisthenumberofuniqueoperatorsandoperandsusedintheprogram. Thus,program vocabulary η=η+η. 156 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) 1 2 ProgramVolume The length of a program (i.e. the total number of operators and operands used in the code)dependsonthechoiceoftheoperatorsandoperandsused.Inotherwords,forthesameprogramming problem,thelengthwoulddependontheprogrammingstyle.Thistypeofdependency would produce different measures of length for essentially the same problem whendifferentprogramminglanguagesareused.Thus,whileexpressingprogramsize,theprogramming languageused must be taken intoconsideration: V =Nlogη 2 Here the program volume V is the minimum number of bits needed to encode the program. Infact,torepresentηdifferentidentifiersuniquely,atleastlogηbits(whereηistheprogram 2 vocabulary)willbeneeded.Inthisscheme,Nlogηbitswillbeneededtostoreaprogramof 2 lengthN.Therefore,thevolumeVrepresentsthesizeoftheprogrambyapproximatelycompensatingfort heeffect of theprogramminglanguageused. PotentialMinimumVolume The potential minimum volume V* is defined as the volume of most succinct program in which aproblem can be coded. The minimum volume is obtained when the program can be expressedusing a single source code instruction. say a function call like foo( ) ;. In other words, the volumeis bound from below due to the fact that a program would have at least two operators and no lessthantherequisite numberof operands. Thus,ifanalgorithmoperatesoninputandoutputdatad,d,…d,themostsuccinctprogram wouldbef(d, d, … d);forwhich η=2, η=n. Therefore,V*=(2+η)log(2+η). 1 2 1 2 n 1 2 n 2 2 2 The program level L is given by L = V*/V. The concept of program level L is introduced in anattempt to measure the level of abstraction provided by the programming language. Using thisdefinition,languages canberanked into levels thatalso appear intuitivelycorrect. The above result implies that the higher the level of a language, the less effort it takes to 157 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) developaprogramusingthatlanguage.Thisresultagreeswiththeintuitivenotionthatittakesmoreeffort to develop a program in assembly language than to develop a program in a high-levellanguageto solve aproblem. EffortandTime The effort required to develop a program can be obtained by dividing the program volume withtheleveloftheprogramminglanguageusedtodevelopthecode.Thus,effortE=V/L,whereEis the number of mental discriminations required to implement the program and also the effortrequired to read and understand the program. Thus, the programming effort E = V²/V* (since L =V*/V) varies as the square of the volume. Experience shows that E is well correlated to the effortneededfor maintenanceofan existingprogram. The programmer’s time T = E/S, where Sthe speed of mentaldiscriminations.The value of Shas been empirically developed from psychological reasoning, and its recommended value forprogrammingapplications is 18. LengthEstimation Even though the length of a program can be found by calculating the total number of operatorsand operands in a program, Halstead suggests a way to determine the length of a program usingthe number of unique operators and operands used in the program. Using this method, theprogram parameters such as length, volume, cost, effort, etc. can be determined even before thestartofanyprogramming activity. His method issummarized below. Halstead assumed that it is quite unlikely that a program has several identical parts – in formallanguageterminology any program of length N consists identicalsubstrings– oflengthgreaterthanη(ηbeingtheprogramvocabulary). In fact, once a piece of code occurs identically at several places, it is made into aprocedure or a function. Thus, it can be assumed that ηuniquestringsoflengthη.Now,itisstandardcombinatorialresultthatforany givenalphabetof r 158 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal of N/

ISBN : 978-81-963532-2-3 (E-Book) sizeK, thereareexactlyKdifferentstrings of length r. Thus. η N/η≤η Or,N≤η Sinceoperatorsandoperandsusuallyalternateinaprogram,theupperboundcanbefurther η1 η2 refinedintoN≤ηη 1 η .Also,Nmustincludenotonlytheorderedsetofnelements,butit 2 shouldalsoincludeallpossiblesubsetsofthatorderedsets,i.e.thepowersetofNstrings(Thisparticularrea soningof Halstead is not veryconvincing!!!). Therefore, N Or,takinglogarithm onbothsides, η1 η2 N=logη+log 2 Soweget, η1 η2 N=log Or, N =logη η1 + logη η2 (η η ) 2 1 2 (approximately,byignoring logη) 2 (η η ) 2 1 2 η1 η2 2= η η η 1 2 η+1 159 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) 2 1 1 2 1 22 = ηlogη+ ηlogη 2 22 Experimental evidence gathered from the analysis of larger number of programs suggests that thecomputed and actual lengths match very closely. However, the results may be inaccurate whensmallprograms when considered individually. In conclusion, Halstead’s theory tries to provide a formal definition and quantification of suchqualitative attributes as program complexity, ease of understanding, and the level of abstractionbasedonsomelowlevelparameterssuchasthenumberofoperands,andoperatorsappearingin theprogram.Halstead’ssoftwarescienceprovidesgrossestimationofpropertiesofalargecollectionofsoftw are, but extendsto individual cases ratherinaccurately. Example: LetusconsiderthefollowingCprogram: main() { inta, b, c, avg; scanf(“%d%d%d”,&a,&b,&c);av g= (a+b+c)/3; printf(“avg=%d”, avg); } Theunique operators are: main,(),{},int,scanf,&,“,”,“;”,=,+,/,printf Theuniqueoperandsare: a, b, c, &a, &b, &c, a+b+c, avg, 3,“%d%d%d”,“avg =%d” Therefore, η= 12, η= 11 1 2 Estimated Length =(12*log12+11*log11) =(12*3.58 + 11*3.45) =(43+38) = 81 160 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) Volume=Length*log(23) = 81*4.52 = 366 COCOMOMODEL Organic,SemidetachedandEmbeddedsoftwareprojects Boehmpostulatedthatany softwaredevelopmentprojectcanbeclassifiedintooneofthefollowing three categories based on the development complexity: organic, semidetached, andembedded.Inordertoclassifyaproductintotheidentifiedcategories,Boehmnotonlyconsideredthec haracteristicsoftheproductbutalsothoseofthedevelopmentteamanddevelopmentenvironment.Roug hlyspeaking,thesethreeproductclassescorrespondtoapplication, utility and system programs, respectively. Normally, data processing programs areconsidered to be application programs. Compilers, linkers, etc., are utility programs. Operatingsystems and real-time system programs, etc. are system programs. System programs interactdirectly withthehardwareandtypically involvemeeting timing constraintsandconcurrentprocessing. Boehm’s[1981]definitionoforganic,semidetached,andembeddedsystemsare elaboratedbelow. Organic: A development project can be considered of organic type, if the project deals withdevelopingawellunderstoodapplicationprogram,thesizeofthedevelopmentteamisreasonablys mall,and the teammembers areexperiencedin developingsimilar typesofprojects. Semidetached:Adevelopmentprojectcanbeconsideredofsemidetachedtype,ifthedevelopment consists of a mixture of experienced and inexperienced staff. Team members mayhave limited experience on related systems but may be unfamiliar with some aspects of thesystembeingdeveloped. Embedded: A development project is considered to be of embedded type, if the software beingdevelopedisstronglycoupledtocomplexhardware,orifthestringentregulationsontheoperational procedures exist. COCOMO COCOMO (Constructive Cost Estimation Model) was proposed by Boehm [1981]. According 161 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) toBoehm,softwarecostestimationshouldbedonethroughthreestages:BasicCOCOMO,Intermediate COCOMO,and Complete COCOMO. BasicCOCOMOModel ThebasicCOCOMOmodelgivesanapproximateestimateoftheprojectparameters.ThebasicCOCOM Oestimation model is given bythefollowingexpressions: a Effort= a 1 1 х(KLOC) PM b 2 Tdev= b x(Effort) Months Where 2 • KLOCistheestimatedsizeofthesoftwareproductexpressedinKiloLinesofCode, • a, a,b, bareconstants foreachcategoryof softwareproducts, 1 2 1 2 • Tdevistheestimatedtimetodevelopthesoftware,expressedinmonths, • Effortisthetotaleffortrequiredtodevelopthesoftwareproduct,expressedinpersonmonth s (PMs). The effort estimation is expressed in units of person-months (PM). It is the area under thepersonmonth plot (as shown in fig. 33.1). It should be carefully noted that an effort of 100 PMdoes not imply that 100 persons should work for 1 month nor does it imply that 1 person shouldbe employed for 100 months, but it denotes the area under the person-month curve (as shown infig.33.1). 162 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) Fig.33.1:Person-monthcurve 163 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) AccordingtoBoehm,everylineofsourcetextshouldbecalculatedasoneLOCirrespectiveof the actual number of instructions on that line. Thus, if a single instruction spans several lines (saynlines),itisconsideredtobenLOC.Thevaluesofa,a,b,bfordifferentcategoriesof 1 2 1 2 products (i.e. organic, semidetached, and embedded) as given by Boehm [1981] are summarizedbelow. He derived the above expressions by examining historical data collected from a largenumberof actual projects. Estimationofdevelopmenteffort For the three classes of software products, the formulas for estimating the effort based on thecodesizeareshown below: 1.05 Organic:Effort=2.4(KLOC) PM 1.12 Semi-detached:Effort=3.0(KLOC) PM 1.20 Embedded:Effort=3.6(KLOC) PM Estimationofdevelopmenttime Forthethreeclassesofsoftwareproducts,theformulasforestimatingthedevelopmenttimebasedon the effort are given below: 0.38 Organic:Tdev=2.5(Effort) Months 0.35 164 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) Semi-detached:Tdev=2.5(Effort) Months 0.32 Embedded:Tdev =2.5(Effort) Months SomeinsightintothebasicCOCOMOmodelcanbeobtainedbyplottingtheestimatedcharacteristics for different software sizes. Fig. 33.2 shows a plot of estimated effort versusproductsize.Fromfig.33.2,wecanobservethattheeffortissomewhatsuperlinearinthesizeof the software product.Thus, the effortrequired to develop a product increases very rapidlywithproject size. Fig.33.2:Effortversusproductsize The development time versus the product size in KLOC is plotted in fig. 33.3. From fig. 33.3, itcan be observed that the development time is a sub linear function of the size of the product, i.e.when the size of the product increases by two times, the time to develop the product does 165 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) notdouble but rises moderately. This can be explained by the fact that for larger products, a largernumberofactivitieswhichcanbecarriedoutconcurrentlycanbeidentified.Theparallelactivities can be carried out simultaneously by the engineers. This reduces the time to completethe project. Further, from fig. 33.3, it can be observed that the development time is roughly thesameforallthethreecategoriesofproducts.Forexample,a60KLOCprogramcanbedeveloped in approximately 18 months, regardless of whether it is of organic, semidetached, orembeddedtype. Fig.33.3:Development timeversus size From the effort estimation, the project cost can be obtained by multiplying the required effort bythemanpowercostpermonth.But,implicitinthisprojectcostcomputationistheassumptionthat the 166 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) entire project cost is incurred on account of the manpower cost alone. In addition tomanpower cost, a project would incur costs due to hardware and software required for the projectandthe companyoverheads foradministration, officespace, etc. It is important to note that the effort and the duration estimations obtained using the COCOMOmodel are called as nominal effort estimate and nominal duration estimate. The term nominalimplies that if anyone tries to complete the project in a time shorter than the estimated duration,thenthecostwillincreasedrastically.But,ifanyonecompletestheprojectoveralongerperiodof time than theestimated, then thereis almost nodecreasein the estimated cost value. Example: Assume that the size of an org organic type software product has been estimated to be 32,000lines of source code. Assume that the average salary of software engineers be Rs. 15,000/permonth.Determinetheeffortrequiredtodevelopthesoftwareproductandthenominaldevelopmentti me. FromthebasicCOCOMOestimationformulafor organicsoftware: 1.05 Effort=2.4х(32) =91PM 0.38 167 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) Nominaldevelopmenttime=2.5 х(91) =14months Costrequiredto developthe product=14 х15,000 =Rs. 210,000/168 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) INTERMEDIATECOCOMOMODEL The basic COCOMO model assumes that effort and development time are functions of theproductsize alone. However, a hostof other projectparameters besides the productsize affectthe effort required to develop the product as well as the development time. Therefore, in order toobtainanaccurateestimationoftheeffortandprojectduration,theeffectofallrelevantparameters must be taken into account. The intermediate COCOMO model recognizes this factand refines the initial estimate obtained using the basic COCOMO expressions by using a set of15 cost drivers (multipliers) based on various attributes of software development. For example, ifmodernprogrammingpracticesareused,theinitialestimatesarescaleddownwardbymultiplication with a cost driver having a value less than 1. If there are stringent reliabilityrequirements on the software product, this initial estimate is scaled upward. Boehm requires theproject manager to rate these 15 different parameters for a particular project on a scale of one tothree. Then, depending on these ratings, he suggests appropriate cost driver values which shouldbe multiplied with the initial estimate obtained using the basic COCOMO. In general, the costdriverscan beclassifiedas beingattributes of the followingitems: Product: The characteristics of the product thatare considered include the inherentcomplexityofthe product, reliabilityrequirements of theproduct, etc. Computer: Characteristics of the computer that are considered include the execution speedrequired,storagespace required etc. Personnel: The attributes of development personnel that are considered include the experiencelevelof personnel, programmingcapability,analysis capability, etc. DevelopmentEnvironment:Developmentenvironmentattributescapturethedevelopmentfacilitiesa vailabletothedevelopers.Animportantparameterthatisconsideredisthesophisticationofthe automation (CASE) toolsusedforsoftwaredevelopment. 169 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) CompleteCOCOMO model AmajorshortcomingofboththebasicandintermediateCOCOMOmodelsisthattheyconsidera software product as a single homogeneous entity. However, most large systems are made upseveral smaller sub-systems. These sub-systems may have widely different characteristics. Forexample, some sub-systems may be considered as organic type, some semidetached, and someembedded.Notonlythattheinherentdevelopmentcomplexityofthesubsystemsmaybedifferent,b utalsoforsomesubsystemsthereliabilityrequirementsmaybehigh,forsomethe development team might have no previous experience of similar development, and so on. Thecomplete COCOMO model considers these differences in characteristics of the subsystems andestimatestheeffortanddevelopmenttimeasthesumoftheestimatesfortheindividualsubsystems. The cost of each subsystem is estimated separately. This approach reduces themarginof errorin thefinal estimate. The following development project can be considered as an example application of the completeCOCOMOmodel.AdistributedManagementInformationSystem(MIS)productforanorgani zation having offices at several places across the country can have the following subcomponents: • Databasepart • GraphicalUserInterface(GUI)part • Communicationpart Of these, the communication part can be considered as embedded software. The database partcould be semi-detached software, and the GUI part organic software. The costs for these threecomponentscanbeestimatedseparately,and summedupto givetheoverall costof thesystem. 170 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) 35 STAFFINGLEVELESTIMATION Once the effort required to develop a software has been determined, it is necessary to determinethe staffing requirement for the project. Putnam first studied the problem of what should be aproper staffing pattern for software projects. He extended the work of Norden who had earlierinvestigated the staffing pattern of research and development (R&D) type of projects. In order toappreciate the staffing pattern of software projects, Norden’s and Putnam’s results must beunderstood. Norden’sWork Norden studied the staffing patterns of several R & D projects. He found that the staffing patterncanbeapproximatedbytheRayleighdistributioncurve(asshowninfig.35.1).Nordenrepresente dtheRayleighcurvebythefollowing equation: -t²/2t² E=K/t² * t *e d d WhereEistheeffortrequiredattimet.Eisanindicationofthenumberofengineers(orthe staffinglevel)atanyparticulartimeduringthedurationoftheproject,Kistheareaunderthecurve,andtisth etimeatwhichthecurveattainsitsmaximumvalue.Itmustberemembered d 171 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) thattheresultsofNordenareapplicabletogeneralR&Dprojectsandwerenotmeanttomodelthestaffingp attern of softwaredevelopment projects. 172 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) Fig.35.1:Rayleigh curve Putnam’sWork Putnamstudiedtheproblemofstaffingofsoftwareprojectsandfoundthatthesoftwaredevelopment has characteristics very similar to other R & D projects studied by Norden and thatthe RayleighNorden curve can be used to relate the number of delivered lines of code to theeffortandthetimerequiredtodeveloptheproject.By analyzingalargenumberofarmyprojects,Putnam derivedthe followingexpression: L= C 1/3 K t k d 4/3 173 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) Thevarious termsof thisexpressionareas follows:  Kisthetotaleffortexpended(inPM)intheproductdevelopmentandListheproductsizein KLOC.  t correspondstothetimeofsystemandintegrationtesting.Therefore,t approximatelyconsideredas thetime requiredtodevelop thesoftware. k d  Cisthestateoftechnologyconstantandreflectsconstraintsthatimpedetheprogressof theprogrammer.TypicalvaluesofC=2forpoordevelopmentenvironment(no k methodology,poordocumentation,andreview,etc.),C k developmentenvironment(softwareengineeringprinciplesare adheredto), C=11foran k excellentenvironment(inadditiontofollowingsoftwareengineeringprinciples,automatedtool sandtechniquesareused).TheexactvalueofCforaspecificprojectcan k becomputedfrom thehistoricaldataofthe organizationdevelopingit. Putnamsuggestedthatoptimalstaffbuild-uponaprojectshouldfollowtheRayleighcurve.Only a small number of engineers are needed at the beginning of a project to carry out planningand specification tasks. As the project progresses and more detailed work is required, the numberof engineers reaches a peak. After implementation and unit testing, the number of project stafffalls. However, the staff build-up should not be carried out in large installments. The team size shouldeither be increased or decreased slowly whenever required to match the Rayleigh-Norden curve.Experienceshowsthataveryrapidbuildupofprojectstaffanytimeduringtheprojectdevelopment correlateswith schedule slippage. Itshouldbeclearthataconstantlevelofmanpowerthroughouttheprojectdurationwouldleadto wastage of effort and increase the time and effort required to develop the product. If a constantnumber of engineers are used over all the phases of a project, some phases would be overstaffedand the other phases would be understaffed causing inefficient use of manpower, leading toscheduleslippageand increasein cost. Effectofschedulechangeoncost Byanalyzing alargenumberof armyprojects, Putnam derived the followingexpression: L= C 1/3 K t k d 174 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal 4/3 =8forgoodsoftware canbe d

ISBN : 978-81-963532-2-3 (E-Book) Where,Kisthetotaleffortexpended(inPM)intheproductdevelopmentandListheproductsizeinKLOC, tcorrespondstothetimeofsystemandintegrationtestingandCisthestateof d technologyconstantand reflectsconstraintsthatimpedetheprogressoftheprogrammer Nowbyusingtheabove expression it is obtained that, 3 4 K= L3/C t k d Or, 4 K= C/t d For the sameproduct size,C = L3/ C3is a constant. or,K1/K2 = td2 k 4/td1 4 or,K1/t d or,cost1/t d (asprojectdevelopmenteffortisequallyproportionaltoprojectdevelopment cost) From the above expression, it can be easily observed that when the schedule of a project iscompressed, the required development effort as well as project development cost increases inproportion to the fourth power of the degree of compression. It means that a relatively smallcompression in delivery schedule can result in substantial penalty of human effort as well asdevelopment cost. For example, if the estimated development time is 1 year, then in order todevelop the product in 6 months, the total effort required to develop the product (and hence theprojectcost) increases 16times. 4 k 175 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) PROJECTSCHEDULING Project-task schedulingis an important projectplanning activity.It involves deciding whichtasks would be taken up when. In order to schedule the project activities, a software projectmanagerneeds to do thefollowing: 1. Identifyallthe tasksneeded tocompletetheproject. 2. Breakdownlargetasksintosmall activities. 3. Determinethe dependencyamongdifferentactivities. 4. Establishthemostlikelyestimatesforthetimedurationsnecessarytocompletetheactivities. 5. Allocateresourcesto activities. 6. Planthestartingandendingdatesforvarious activities. 7. Determinethecriticalpath.Acriticalpathisthechainofactivitiesthatdeterminesthedurationo fthe project. The first step in scheduling a software project involves identifying all the tasks necessary tocomplete the project. A good knowledge of the intricacies of the project and the developmentprocess helps the managers to effectively identify the important tasks of the project. Next, thelarge tasks are broken down into a logical set of small activities which would be assigned todifferent engineers. The work breakdown structure formalism helps the manager to breakdownthetaskssystematicallyaftertheprojectmanagerhasbrokendownthetasksandcreatedthewo rk breakdown structure, he has to find the dependency among the activities. Dependencyamong the different activities determines the order in which the different activities would becarried out. If an activity A requires the results of another activity B, then activity A must bescheduled after activity B. In general, the task dependencies define a partial ordering amongtasks, i.e. each tasks may precede a subset of other tasks, but some tasks might not have anyprecedence ordering defined between them (called concurrent task). The dependency among theactivitiesisrepresented in theform ofanactivitynetwork. Once the activity network representation has been worked out, resources are allocated to eachactivity. Resource allocation is typically done using a Gantt chart. After resource allocation isdone, a PERT chart representation is developed. The PERT chart representation is suitable forprogram monitoring and control. For task scheduling, the project manager needs to decomposetheprojecttasksintoasetofactivities.Thetimeframewheneachactivityistobeperformedisto be determined. The end of each activity is called milestone. The project manager tracks 176 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) theprogress of a project by monitoring the timely completion of the milestones. If he observes thatthe milestones start getting delayed, then he has to carefully control the activities, so that theoveralldeadlinecan still be met. WorkBreakdownStructure Work Breakdown Structure (WBS) is used to decompose a given task set recursively into smallactivities. WBS provides a notation for representing the major tasks need to be carried out inorder to solve a problem. The root of the tree is labeled by the problem name. Each node of thetree is broken down into smaller activities that are made the children of the node. Each activity isrecursively decomposed into smaller sub-activities until at the leaf level, the activities requiresapproximately two weeks to develop. Fig. 36.1 represents the WBS of a MIS (ManagementInformationSystem)software. Whilebreakingdownataskintosmallertasks,themanagerhastomakesomeharddecisions.Ifa task is broken down into large number of very small activities, these can be carried outindependently.Thus,itbecomespossibletodeveloptheproductfaster(withthehelpofadditional manpower). Therefore, to be able to complete a project in the least amount of time, themanagerneedstobreaklargetasksintosmallerones,expectingtofindmoreparallelism.However, it is not useful to subdivide tasks into units which take less than a week or two toexecute. Very fine subdivision means that a disproportionate amount of time must be spent onpreparingand revisingvarious charts. 177 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) Fig.36.1:Workbreakdownstructureof anMIS problem Activity networks and critical path method WBS representation of a project is transformed intoanactivitynetworkbyrepresentingactivitiesidentifiedinWBSalongwiththeirinterdependencies. An activity network shows the different activities making up a project, theirestimated durations, and interdependencies (as shown infig. 36.2). Each activity is representedbyarectangularnodeand the duration oftheactivityis shown alongside each task. Managers can estimate the time durations for the different tasks in several ways. One possibilityis that they can empirically assign durations to different tasks. This however is not a good idea,because software engineers often resent such unilateral decisions. A possible alternative is to letengineer himself estimate the time for an activity he can assigned to. However, some managersprefer to estimate the time for various activities themselves. Many managers believe that anaggressive schedule motivates the engineers to do a better and faster job. 178 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) However, carefulexperiments have shown that unrealistically aggressive schedules not only cause engineers tocompromiseonintangiblequalityaspects,butalsoareacauseforscheduledelays.Agoodwaytoachiev eaccuratelyinestimationofthetaskdurationswithoutcreatingundueschedulepressuresis havepeople set their own schedules. to Fig.36.2:Activitynetworkrepresentationofthe MISproblem CriticalPathMethod(CPM) From the activity network representation following analysis can be made. The minimum time(MT) to complete the project is the maximum of all paths from start to finish. The earliest start(ES)timeofataskisthemaximumofallpathsfromthestarttothetask.Thelateststarttimeis the difference between MT and the maximum of all paths from this task to the finish. The earliestfinish time (EF) of a task is the sum of the earliest start time of the task and the duration of thetask. The latest finish (LF) time of a task can be obtained by subtracting maximum of all pathsfrom this task to finish from MT. The slack time (ST) is LS – EF and equivalently can be writtenas LF – EF. The slack time (or float time) is the total time that a task may be delayed before itwill affect the end time of the project. The slack time indicates the “flexibility” in 179 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) starting andcompletionoftasks.Acriticaltaskisonewithazeroslacktime.Apathfromthestartnodetothe finish node containing only critical tasks is called a critical path. These parameters fordifferenttasksforthe MIS problem areshown in thefollowingtable. Task Specification Designdatabase DesignGUIpart Codedatabase CodeGUIpart Integrateandtest Writeusermanual ES 0 15 15 60 45 165 15 EF 15 60 45 165 90 285 75 LS 0 15 90 60 120 165 225 LF 15 60 120 165 165 285 285 ST 0 0 75 0 75 0 210 The critical paths are all the paths whose duration equals MT. The critical path in fig. 36.2 isshownwith ablue arrow. GanttChart Ganttchartsaremainlyusedtoallocateresourcestoactivities.Theresourcesallocatedtoactivities include staff, hardware, and software. Gantt charts (named after its developer HenryGantt) are useful for resource planning. A Gantt chart is a special type ofbar chart where eachbar represents an activity. The bars are drawn along a time line. The length of each bar isproportionalto theduration oftime planned forthecorrespondingactivity. Gantt charts are used in software project management are actually an enhanced version of thestandard Ganttcharts.In the Ganttchartsusedfor softwareprojectmanagement,eachbarconsists of a white part and a shaded part. The shaded part of the bar shows the length of timeeach task is estimated to take. The white part shows the slack time, that is, the latest time bywhich a task must be finished. A Gantt chart representation for the MIS problem of fig. 36.2 isshownin thefig. 36.3. 180 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) Fig.36.3:Ganttchart representationoftheMISproblem PERTChart PERT (Project Evaluation and Review Technique) charts consist of a network of boxes andarrows. The boxes represent activities and the arrows represent task dependencies. PERT chartrepresents the statistical variations in the project estimates assuming a normal distribution. Thus,in a PERT chart instead of making a single estimate for each task, pessimistic, likely, andoptimisticestimatesaremade.TheboxesofPERTchartsareusuallyannotatedwiththepessimistic, likely, and optimistic estimates for every task. Since all possible completion timesbetween the minimum and maximum duration for every task has to be considered, there are notone but many critical paths, depending on the permutations of the estimates for each task. Thismakes critical path analysis in PERT charts very complex. A critical path in a PERT chart isshown by using thicker arrows. The PERT chart representation of the MIS problem of fig. 36.2 isshown in fig. 36.4. PERT charts are a more sophisticated form of activity chart. In activitydiagrams only 181 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) theestimatedtask durations are represented. Since, the actual durations mightvaryfrom theestimateddurations, theutilityoftheactivitydiagrams arelimited. Gantt chart representation of a project schedule is helpful in planning the utilization of resources,while PERT chart is useful for monitoring the timely progress of activities. Also, it is easier toidentify parallel activities in a project using a PERT chart. Project managers need to identify theparallelactivities in aprojectforassignment to differentengineers. Fig.36.4:PERTchartrepresentationoftheMISproblem 182 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) 183 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) ORGANIZATIONSTRUCTURE Usually every software development organization handles several projects at any time. Softwareorganizations assign different teams of engineers to handle different software projects. Each typeof organization structure has its own advantages and disadvantages so the issue “how is theorganization as a whole structured?” must be taken into consideration so that each softwareprojectcan befinishedbeforeits deadline. Functionalformatvs.projectformat There are essentially two broad ways in which a software development organization can bestructured: functional format and project format. In the project format, the project developmentstaffs are divided based on the project for which they work (as shown in fig. 37.1). In thefunctional format, the developmentstaffs are divided based on the functional group to whichthey belong. The different projects borrow engineers from the required functional groups forspecific phases to be undertaken in the project and return them to the functional group upon thecompletionofthe phase. 184 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) a) ProjectOrganization b) FunctionalOrganization Fig.37.1:Schematicrepresentationof thefunctionalandprojectorganization In the functional format, different teams of programmers perform different phases of a project.For example, one team might do the requirements specification, another do the design, and so on.Thepartiallycompletedproductpassesfromoneteamtoanotherastheprojectevolves.Therefore, the functional format requires considerable communication among the different teamsbecause the work of one team must be clearly understood by the subsequent teams working ontheproject. Thisrequiresgoodqualitydocumentationto be producedaftereveryactivity. In the project format, a set of engineers is assigned to the project at the start of the project andthey remain with the project till the completion of the project. Thus, the same team carries out allthe life cycle activities. Obviously, the functional format requires more communication amongteams than the project format, because one team must understand the work done by the previousteams. Advantagesoffunctionalorganization overprojectorganization 185 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) Even though greater communication among the team members may appear as an avoidableoverhead, the functional format has many advantages. The main advantages of a functionalorganizationare: • Easeofstaffing 186 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) • Production ofgood qualitydocuments • Jobspecialization • Efficienthandlingoftheproblemsassociatedwithmanpowerturnover. The functional organization allows the engineers to become specialists in particular roles, e.g.requirements analysis, design, coding, testing, maintenance, etc. They perform these roles againand again for different projects and develop deep insights to their work. It also results in moreattentionbeingpaidtoproperdocumentationattheendofaphasebecauseofthegreaterneedfor clear communication as between teams doing different phases. The functional organizationalso provides an efficient solution to the staffing problem. We have already seen that the staffingpattern should approximately follow the Rayleigh distribution for efficient utilization of thepersonnel by minimizing their wait times. The project staffing problem is eased significantlybecause personnel can be brought onto a project as needed, and returned to the functional groupwhen they are no more needed. This possibly is the most important advantage of the functionalorganization. A project organization structure forces the manager to take in almost a constantnumber of engineers for the entire duration of his project. This results in engineers idling in theinitial phase of the software development and are under tremendous pressure in the later phase ofthe development. A further advantage of the functional organization is that it is more effective inhandling the problem of manpower turnover. This is because engineers can be brought in fromthe functional pool when needed. Also, this organization mandates production of good qualitydocuments,so newengineers can quicklyget usedto thework alreadydone. Unsuitabilityoffunctionalformatin smallorganizations In spite of several advantages of the functional organization, it is not very popular in the softwareindustry.The apparentparadoxisnotdifficulttoexplain.The projectformatprovidesjobrotationtotheteammembers.Thatis,eachteammembertakesontheroleofthe designer,coder, tester, etc during the course of the project. On the other hand, considering the present skillshortage, it would be very difficult for the functional organizations to fill in slots for some rolessuch as maintenance, testing, and coding groups. Also, another problem with the functionalorganizationisthatifanorganizationhandlesprojectsrequiringknowledgeofspecializeddo main areas, then these domain experts cannot be brought in and out of the project for thedifferent phases, unless the company handles a large number of such projects. Also, for obviousreasons the functional format is not suitable for small organizations handling just one or twoprojects. TeamStructures Team structure addresses the issue of organization of the individual project teams. There aresome possible ways in which the individual project teams can be organized. There are mainlythreeformalteamstructures:chiefprogrammer,democratic,andthemixedteamorganizations. 187 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) althoughseveralothervariationstothesestructuresarepossible.Problemsofdifferentcomplexitiesand sizesoften requiredifferentteamstructures forchiefsolution. ChiefProgrammerTeam In this team organization, a senior engineer provides the technical leadership and is designated asthe chief programmer. The chief programmer partitions the task into small activities and assignsthem to the team members. He also verifies and integrates the products developed by differentteam members. The structure of the chief programmer team is shown in fig. 37.2. The chiefprogrammerprovidesanauthority,andthisstructureisarguablymoreefficientthanthedemocratic team for well-understood problems. However, the chief programmer team leads tolowerteammorale,sinceteam-membersworkundertheconstantsupervisionofthechiefprogrammer. This also inhibits their original thinking. The chief programmer team is subject tosinglepointfailuresincetoomuchresponsibilityandauthorityisassignedtothechiefprogrammer. Fig.37.2:Chiefprogrammerteamstructure The chief programmer team is probably the most efficient way of completing simple and smallprojects since the chief programmer can work out a satisfactory design and ask the programmersto code different modules of his design solution. For example, suppose an 188 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) organization hassuccessfullycompletedmanysimpleMISprojects.Then,forasimilarMISproject,chiefprogrammer team structure can be adopted. The chief programmer team structure works wellwhenthetaskiswithintheintellectualgraspofasingleindividual.However,evenforsimple andwellunderstoodproblems,anorganizationmustbeselectiveinadoptingthechiefprogrammerstructure.Thec hiefprogrammerteamstructureshouldnotbeusedunlesstheimportance of early project completion outweighs other factors such as team morale, personaldevelopments,life-cyclecost etc. DemocraticTeam The democratic team structure, as the name implies, does not enforce any formal team hierarchy(asshowninfig.37.3).Typically,amanagerprovidestheadministrativeleadership.Atdiffere nttimes,different membersof thegroup providetechnical leadership. Fig.37.3:Democraticteamstructure The democratic organization leads to higher morale and job satisfaction. Consequently, it suffersfrom less man-power turnover. Also, democratic team structure is appropriate for less 189 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) understoodproblems, since a group of engineers can invent better solutions than a single individual as in achief programmer team. A democratic team structure is suitable for projects requiring less thanfiveorsixengineersandforresearchorientedprojects.Forlargesizedprojects,apuredemocratic organization tends to become chaotic. The democratic team organization encouragesegolessprogrammingasprogrammers can shareand reviewoneanother’s work. 190 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) RISKMANAGEMENT 191 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) Asoftwareprojectcanbeaffectedbyalargevarietyofrisks.Inordertobeabletosystematicallyidentifythe importantriskswhichmightaffectasoftwareproject,itisnecessaryto categorize risks into different classes. The project manager can then examine which risks fromeachclass arerelevant to the project. Therearethreemaincategoriesofrisks whichcanaffectasoftwareproject: 1. Projectrisks Projectrisksconcernvariesformsofbudgetary,schedule,personnel,resource,andcustomerrelatedproblems.Animportantprojectriskisscheduleslippage.Since,software is intangible, it is very difficult to monitor and control a software project. It isvery difficult to control something which cannot be seen. For any manufacturing project,such as manufacturing of cars, the project manager can see the product taking shape. Hecan for instance, see that the engine is fitted, after that the doors are fitted, the car isgetting painted, etc. Thus he can easily assess the progress of the work and control it. Theinvisibility of the product being developed is an important reason why many softwareprojectssuffer from therisk of schedule slippage. 2. Technicalrisks Technicalrisksconcernpotentialdesign,implementation,interfacing,testing,andmaintenance problems. Technical risks also include ambiguous specification, incompletespecification, changing specification, technical uncertainty, and technical obsolescence.Most technical risks occur due to the development team’s insufficient knowledge abouttheproject. 3. Businessrisks This type of risks include risks of building an excellent product that no one wants, losingbudgetaryor personnel commitments, etc. RiskAssessment Theobjectiveofriskassessmentistoranktherisksintermsoftheirdamagecausingpotential.Forrisk assessment, first each riskshould beratedin two ways: • Thelikelihoodofariskcomingtrue (denoted asr). • Theconsequenceofthe problemsassociatedwiththatrisk(denotedass). Basedon thesetwo factors, thepriorityof each risk can becomputed: 192 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) p =r* s Where, p is the priority with which the risk must be handled, r is the probability of the riskbecoming true, and s is the severity of damage caused due to the risk becoming true. If allidentified risks are prioritized, then the most likely and damaging risks can be handled first andmorecomprehensive riskabatementprocedures can bedesignedfor theserisks. RiskContainment After all the identified risks of a project are assessed, plans must be made to contain the mostdamaging and the most likely risks. Different risks require different containment procedures. Infact,most risks requireingenuityon the part oftheproject manager in tacklingthe risk. Therearethreemain strategiestoplanforriskcontainment: Avoid the risk- This may take several forms such as discussing with the customer to change therequirements to reduce the scope of the work, giving incentives to the engineers to avoid the riskofmanpower turnover,etc. Transfer the risk- This strategy involves getting the risky component developed by athirdparty,buyinginsurancecover, etc. Riskreduction-Thisinvolvesplanningwaystocontainthedamageduetoarisk.Forexample,ifthereis risk that somekeypersonnel mightleave, new recruitment maybeplanned. RiskLeverage To choose between the different strategies of handling a risk, the project manager must considerthe cost of handling the risk and the corresponding reduction of risk. For this the risk leverage ofthedifferent risks can becomputed. Risk leverage is the difference in risk exposure divided by the cost of reducing the risk. Moreformally, riskleverage=(riskexposurebeforereduction– riskexposureafterreduction)/(costofreduction) Riskrelatedtoscheduleslippage Even though there are three broad ways to handle any risk, but still risk handling requires a lot ofingenuity on the part of a project manager. As an example, it can be considered the optionsavailable to contain an important type of risk that occurs in many software projects – that 193 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) ofscheduleslippage.Risksrelatingtoscheduleslippageariseprimarilyduetotheintangiblenature of software.Therefore,these canbe dealtwithby increasing the visibility of the softwareproduct. Visibility of a software product can be increased by producing relevant documentsduring the development process wherever meaningful and getting these documents reviewed byanappropriateteam.Milestonesshouldbeplacedatregularintervalsthroughasoftwareengineering process to provide a manager with regular indication of progress. Completion of aphase of the development process before followed need not be the only milestones. Every phasecan be broken down to reasonable-sized tasks and milestones can be scheduled for these taskstoo. A milestone is reached, once documentation produced as part of a software engineering taskis produced and gets successfully reviewed. Milestones need not be placed for every activity. Anapproximaterule ofthumb is to set amilestoneevery10 to 15 days. SoftwareConfigurationManagement The results (also called as the deliverables) of a large software development effort typicallyconsist of a large number of objects, e.g. source code, design document, SRS document, testdocument, user’s manual, etc. These objects are usually referred to and modified by a number ofsoftware engineers through out the life cycle of the software. The state of all these objects at anypoint of time is called the configuration of the software product. The state of each deliverableobjectchanges asdevelopment progressesandalso as bugsaredetectedandfixed. Releasevs.Versionvs.Revision A new version of a software is created when there is a significant change in functionality,technology, or the hardware it runs on, etc. On the other hand a new revision of a software refersto minor bug fix in that software. A new release is created if there is only a bug fix, minorenhancementsto the functionality, usability, etc. For example, one version of a mathematical computation package might run on Unixbasedmachines, another on Microsoft Windows and so on. As a software is released and used by thecustomer, errors are discovered that need correction. Enhancements to the functionalities of thesoftware may also be needed. A new release of software is an improved system intended toreplace an old one. Often systems are described as version m, release n; or simple m.n. Formally,a history relation is version of can be defined between objects. This relation can be split into twosubrelations is revision ofand is variant of. Necessityofsoftwareconfigurationmanagement There are several reasons for putting an object under configuration management. But, possiblythe most important reason for configuration management is to control the access to the differentdeliverableobjects.Unlessstrictdisciplineisenforcedregardingupdationandstorageofdiffere nt objects, severalproblems appear. Thefollowing are some of the important problemsthatappear 194 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) if configuration management is not used.  Inconsistency problem when the objects are replicated. A scenario can be consideredwhere every software engineer has a personal copy of an object (e.g. source code). Aseach engineer makes changes to his local copy, he is expected to intimate them to otherengineers, so that the changes in interfaces are uniformly changed across all modules.However, many times an engineer makes changes to the interfaces in his local copies andforgets to intimate other teammates about the changes. This makes the different copies oftheobjectinconsistent.Finally,whentheproductisintegrated,itdoesnotwork.Therefore, when several team members work on developing an object, it is necessary forthem to work onasinglecopyoftheobject, otherwiseinconsistencymayarise.  Problemsassociatedwithconcurrentaccess.Supposethereisasinglecopy ofaproblemmodule,andseveralengineersareworkingonit.Twoengineersmaysimultaneously carry out changes to different portions of the same module, and whilesaving overwrite each other. Though the problem associated with concurrent access toprogramcodehasbeenexplained,similarproblemsoccurforany otherdeliverableobject.  Providing a stable development environment. When a project is underway, the teammembers need a stable environment to make progress. Suppose somebody is trying tointegrate module A, with the modules B and C, he cannot make progress if developer ofmodule C keeps changing C; this can be especially frustrating if a change to module Cforces him to recompile A. When an effective configuration management is in place, themanager freezes the objects to form a base line. When anyone needs any of the objectsunderconfigurationcontrol,heisprovidedwithacopy ofthebaselineitem.Therequester makes changes to his private copy. Only after the requester is through with allmodifications to his private copy, the configuration is updated and a new base line getsformed instantly. This establishes a baseline for others to use and depend on. Also,configuration may be frozen periodically. Freezing a configuration may involve archivingeverything needed to rebuild it. (Archiving means copying to a safe place such as amagnetictape).  System accountingandmaintainingstatusinformation.Systemaccountingkeepstrackof who madeaparticular changeand whenthechangewas made.  Handling variants. Existence of variants of a software product causes some peculiarproblems. Suppose somebody has several variants of the same module, and finds a bug inone of them. Then, it has to be fixed in all versions and revisions. To do it 195 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) efficiently, heshouldnothaveto ofthesoftwareseparately. SoftwareConfigurationManagementActivities Normally,aprojectmanagerperformstheconfigurationmanagementactivitybyusinganautomatedcon figurationmanagementtool.Aconfigurationmanagementtoolprovidesautomatedsupportforovercom ingalltheproblemsmentionedabove.Inaddition,aconfiguration management tool helps to keep track of various deliverable objects, so that theproject manager can quickly and unambiguously determine the current state of the project. Theconfiguration management tool enables the engineers to change the various components in acontrolledmanner. Configurationmanagementiscarriedoutthroughtwoprincipalactivities: • Configurationidentificationinvolvesdecidingwhichpartsofthesystemshouldbekepttrackof . • Configurationcontrolensuresthatchangestoasystemhappensmoothly. ConfigurationIdentification The project manager normally classifies the objects associated with a software developmenteffort into three main categories: controlled, pre controlled, and uncontrolled. Controlled objectsare those which are already put under configuration control. One must follow some formalprocedures to change them. Pre controlled objects are not yet under configuration control, butwill eventually be under configuration control. Uncontrolled objects are not and will not besubjectedtoconfigurationcontrol.Controllableobjectsincludebothcontrolledandprecontrolledobj ects. Typical controllable objects include: • Requirementsspecificationdocument • Designdocuments Toolsusedtobuildthesystem,such ascompilers,linkers,lexicalanalyzers,parsers, etc. • Sourcecodeforeachmodule • Testcases • Problemreports The configuration management plan is written during the project planning phase and it lists allcontrolled objects. The managers who develop the plan must strike a balance between controllingtoo much, and controlling too little. If too much is controlled, overheads due to configurationmanagement increase to unreasonably high levels. On the other hand, controlling too little mightleadto confusion when somethingchanges. 196 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal fixitin eachandeveryversionandrevision

ISBN : 978-81-963532-2-3 (E-Book) Configuration Control Configuration control is the process of managing changes to controlled objects. Configurationcontrol is the part of a configuration management system that most directly affects the day-to-dayoperationsofdevelopers.Theconfigurationcontrolsystem preventsunauthorized changesto any controlled objects. In order to change a controlled object such as a module, a developer canget a private copy of the module by a reserve operation as shown in fig. 38.1. Configurationmanagement tools allow only one person to reserve a module at a time. Once an object isreserved, it does not allow anyone else to reserve this module until the reserved module isrestored as shown in fig. 38.1. Thus, by preventing more than one engineer to simultaneouslyreserveamodule, the problems associatedwith concurrentaccess aresolved. Fig.38.1:Reserveandrestoreoperationinconfigurationcontrol It can be shown how the changes to any object that is under configuration control can beachieved. The engineer needing to change a module first obtains a private copy of the 197 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) modulethrough a reserve operation. Then, he carries out all necessary changes on this private copy.However, restoring the changed module to the system configuration requires the permission of achange control board (CCB). The CCB is usually constituted from among the development teammembers. For every change that needs to be carried out, the CCB reviews the changes made tothecontrolled object andcertifiesseveral things about thechange: 1. Changeiswell-motivated. 2. Developerhasconsideredanddocumentedthe effectsofthechange. 3. Changesinteract wellwiththechangesmadebyotherdevelopers. 4. Appropriatepeople(CCB)havevalidatedthechange,e.g.someonehastestedthechangedcode,an d has verifiedthat thechangeis consistent with therequirement. The change control board (CCB) sounds like a group of people. However, except for verylarge projects, the functions of the change control board are normally discharged by theproject manager himself or some senior member of the development team. Once the CCBreviews the changes to the module, the project manager updates the old base line through arestore operation (as shown in fig. 38.1). A configuration control tool does not allow adevelopertoreplaceanobjecthehasreservedwithhislocalcopyunlesshegetsanauthorization from the CCB. By constraining the developers’ ability to replace reservedobjects, a stable environment is achieved. Since a configuration management tool allows onlyone engineer to work on one module at any one time, problem of accidental overwriting iseliminated. Also, since only the manager can update the baseline after the CCB approval,unintentionalchanges are eliminated. ConfigurationManagementTools SCCSandRCSaretwopopularconfigurationmanagementtoolsavailableonmostUNIXsystems. SCCS or RCS can be used for controlling and managing different versions of text files.SCCS and RCS do not handle binary files (i.e. executable files, documents, files containingdiagrams, etc.) SCCS and RCS provide an efficient way of storing versions that minimizes theamount of occupied disk space. Suppose, a module MOD is present in three versions MOD1.1,MOD1.2, 198 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) and MOD1.3. Then, SCCS and RCS stores the original module MOD1.1 together withchanges needed to transform MOD1.1 into MOD1.2 and MOD1.2 to MOD1.3. The changesneeded to transform each base lined file to the next version are stored and are called deltas. Themain reason behind storing the deltas rather than storing the full version files is to save diskspace.ThechangecontrolfacilitiesprovidedbySCCSandRCSincludetheabilitytoincorporate restrictions on the set of individuals who can create new versions, and facilities forchecking componentsinandout(i.e.reserveandrestoreoperations).Individualdeveloperscheck out components and modify them. After they have made all necessary changes to a moduleand after the changes have been reviewed, they check in the changed module into SCCS or RCS.Revisions are denoted by numbers in ascending order, e.g., 1.1, 1.2, 1.3 etc. It is also possible tocreatevariants or revisions of acomponentbycreatingafork in thedevelopmenthistory. 199 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) COMPUTERAIDEDSOFTWAREENGINEERING CASEtool andits scope A CASE (Computer Aided Software Engineering) tool is a generic term used to denote any formof automated support for software engineering. In a more restrictive sense, a CASE tool meansany toolused toautomatesomeactivity associatedwithsoftware development.Many CASEtools are available. Some of these CASE tools assist in phase related tasks such as specification,structured analysis, design, coding, testing, etc.; and others to non-phase activities such as projectmanagementandconfiguration management. ReasonsforusingCASE tools Theprimaryreasons forusingaCASE tool are: • Toincreaseproductivity • Tohelpproducebetter qualitysoftwareatlower cost CASEenvironment AlthoughindividualCASEtoolsareuseful,thetruepowerofatoolsetcanberealizedonlywhen these set of tools are integrated into a common framework or environment. CASE tools arecharacterized by the stage or stages of software development life cycle on which they focus.Since different tools covering different stages share common information, it is required that theyintegrate through somecentralrepository tohave a consistentview of informationassociatedwith the software development artifacts. This central repository is usually a data dictionarycontainingthe definition ofall compositeand elementary data items. Through the central repository all the CASE tools in a CASE environment sharecommon information among themselves. Thus a CASE environment facilities the automation ofthe step-by-step methodologies for software development. A schematic representation of a CASEenvironmentis shown in fig. 39.1. 200 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) Fig.39.1:A CASEEnvironment CASEenvironmentvsprogrammingenvironment A CASE environment facilitates the automation of the step-by-step methodologies for softwaredevelopment. In contrast to a CASE environment, a programming environment is an integratedcollection oftools to support onlythe codingphaseof softwaredevelopment. Benefitsof CASE Several benefits accrue from the use of a CASE environment or even isolated CASE tools. Someofthose benefits are:  A key benefit arising out of the use of a CASE environment is cost saving through alldevelopment phases. Different studies carry out to measure the impact of CASE put theeffortreduction between30%to 40%. 201 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book)  Use of CASE tools leads to considerable improvements to quality. This is mainly due tothefactsthatonecaneffortlesslyiteratethroughthedifferentphasesofsoftwaredevelopmenta nd thechances of humanerror areconsiderablyreduced.  CASEtoolshelpproducehighqualityandconsistentdocuments.Sincetheimportantdata relating to a software product are maintained in a central repository, redundancy inthe stored data is reduced and therefore chances of inconsistent documentation is reducedtoa great extent.  CASE tools take out most of the drudgery in a software engineer’s work. For example,they need not check meticulously the balancing of the DFDs but can do it effortlesslythroughthe press ofabutton.  CASE tools have led to revolutionary cost saving in software maintenance efforts. Thisarises not only due to the tremendous value of a CASE environment in traceability andconsistency checks, but also due to the systematic information capture during the variousphasesof softwaredevelopment as a resultofadheringtoaCASE environment.  IntroductionofaCASEenvironmenthasanimpactonthestyleofworkingofacompany,and makes it oriented towards thestructured and orderlyapproach. RequirementsofaprototypingCASEtool Prototypingisusefultounderstandtherequirementsofcomplexsoftwareproducts,todemonstrate a concept, to market new ideas, and so on. The important features of a prototypingCASEtool areas follows: • Defineuserinteraction • Definethesystemcontrolflow • Storeandretrievedatarequiredbythesystem • Incorporatesomeprocessinglogic Featuresof agoodprototypingCASEtool There are several stand-alone prototyping tools. But a tool that integrates with the data dictionarycan make use of the entries in the data dictionary, help in populating the data dictionary andensure theconsistency betweenthedesigndataandtheprototype.Agoodprototyping toolshouldsupport the followingfeatures:  Sinceoneofthemainuses ofaprototypingCASE toolis graphicaluserinterface(GUI)development, prototyping CASE tool should support the 202 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) user to create a GUI using agraphics editor. The user should be allowed to define all data entry forms, menus andcontrols.  Itshould integratewith thedatadictionaryof aCASE environment.  If possible, it should be able to integrate with external user defined modules written in Corsomepopularhigh level programminglanguages.  Theusershouldbeabletodefinethesequenceofstatesthroughwhichacreatedprototypecanrun. Theusershouldalsobeallowedtocontroltherunningoftheprototype.  he run time system of prototype should support mock runs of the actual system andmanagementofthe inputand output data. Structuredanalysisanddesign withCASEtools Several diagramming techniques are used for structured analysis and structured design. Thefollowingsupports mightbeavailablefrom CASE tools.  ACASEtoolshouldsupportoneormoreofthestructuredanalysisanddesigntechniques.  Itshouldsupporteffortlesslydrawinganalysisanddesigndiagrams.  It should support drawing for fairly complex diagrams, preferably through a hierarchy oflevels.  The CASE tool should provide easy navigation through the different levels and throughthedesignand analysis.  The tool must support completeness and consistency checking across the design andanalysis and through all levels of analysis hierarchy. Whenever it is possible, the systemshould disallow any inconsistent operation, but it may be very difficult to implement sucha feature. Whenever there arises heavy computational load while consistency checking, itshouldbepossible to temporarilydisable consistencychecking. Codegeneration andCASEtools As far as code generation is concerned, the general expectation of a CASE tool is quite low. Areasonablerequirementistraceabilityfromsourcefiletodesigndata.More supportsexpectedfrom aCASE tool duringcodegeneration phasearethe following: pragmatic  The CASE tool should support generation of module skeletons or templates in one ormorepopularlanguages.Itshouldbepossibletoincludecopyrightmessage,briefdescription of the module, author name and thedate of creation in some selectableformat.  Thetoolshouldgeneraterecords,structures,classdefinitionautomaticallyfromthecontentsof thedata dictionaryin one ormorepopularlanguages.  Itshouldgeneratedatabasetablesforrelationaldatabasemanagementsystems. 203 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book)  The tool should generate code for user interface from prototype definition for X windowandMS window based applications. Testcasegeneration CASEtool TheCASEtool fortest case generationshould havethe followingfeatures:  Itshouldsupportbothdesignandrequirementtesting.  It should generate test set reports in ASCII format which can be directly imported into thetestplan document. 204 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) Hardwareandenvironmentalrequirements In most cases, it is the existing hardware that would place constraints upon the CASE toolselection. Thus, instead of defining hardware requirements for a CASE tool, the task at handbecomes to fit in an optimal configuration of CASE tool in the existing hardware capabilities.Therefore, it can be emphasized on selecting the most optimal CASE tool configuration for agivenhardware configuration. The heterogeneous network is one instance of distributed environment and this can be chosen forillustrationasit ismorepopularduetoits machineindependent features.TheCASEtoolimplementation in heterogeneous network makes use of client-server paradigm. The multipleclients who run different modules access data dictionary through this server. The data dictionaryserver may support one or more projects. Though it is possible to run many servers for differentprojectsbut distributed implementation of data dictionaryis not common. The tool set is integrated through the data dictionary which supports multiple projects, multipleusers working simultaneously and allows sharing information between users and projects. Thedata dictionary provides consistent view of all project entities, e.g. a data record definition and itsentity-relationshipdiagrambeconsistent.Theservershoulddepicttheperprojectlogicalviewof the data dictionary. This means that it should allow back up/restore, copy, cleaning part of thedatadictionary, etc. Thetoolshouldworksatisfactorilyformaximumpossiblenumberofusersworkingsimultaneously. The tool should support multi-windowing environment for the users. This isimportant to enable the users to see more than one diagram at a time. It also facilitates navigationandswitching from one part to theother. DocumentationSupport The deliverable documents shouldbe organized graphically and shouldbe able to incorporatetext and diagrams from the central repository. This helps in producing up-to-date documentation.TheCASEtoolshouldintegratewithoneormoreofthecommerciallyavailabledesktopp ublishing packages. It should be possible to export text, graphics, tables, data dictionary reportstotheDTP packagein standard forms such as PostScript. ProjectManagementSupport The CASE tool should support collecting, storing, and analyzing information on the softwareproject’s progress such as the estimated task duration, scheduled and actual task start, completiondate,dates andresults ofthe reviews,etc. 205 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) 206 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) ExternalInterface The CASE tool should allow exchange of information for reusability of design. The informationwhich istobeexported by the CASEtoolshouldbe preferably inASCII formatand supportopenarchitecture.Similarly,thedatadictionary shouldprovideaprogramminginterfacetoaccess information. It is required for integration of custom utilities, building new techniques, orpopulatingthe datadictionary. ReverseEngineering The CASE tool should support generation of structure charts and data dictionaries from theexisting source codes. It should populate the data dictionary from the source code. If the tool isused for re-engineering information systems, it should contain conversion tool from indexedsequentialfilestructure,hierarchicalandnetworkdatabasetorelational databasesystems. DataDictionaryInterface The data dictionary interface should provide view and update access to the entities and relationsstored in it. It should have print facility to obtain hard copy of the viewed screens. It shouldprovide analysis reports like cross-referencing, impact analysis, etc. Ideally, it should support aquerylanguageto view its contents. Second-generationCASEtool An important feature of the second-generation CASE tool is the direct support of any adaptedmethodology. This would necessitate the function of a CASE administrator organization who cantailor the CASE tool to a particular methodology. In addition, the second-generation CASE toolshavefollowingfeatures:  Intelligent diagramming support- The fact that diagramming techniques are useful forsystemanalysisanddesigniswellestablished.ThefutureCASEtoolswouldprovidehelpto aestheticallyandautomaticallylayout thediagrams.  IntegrationwithimplementationenvironmentTheCASEtoolsshouldprovideintegrationbetween design and implementation.  Data dictionary standards- The user should be allowed to integrate many developmenttools into one environment. It is highly unlikely that any one vendor will be able todeliveratotalsolution.Moreover,apreferredtoolwouldrequiretuningupforaparticular system. Thus the user would act as a system integrator. This is possibly only ifsomestandard ondata dictionaryemerges.  Customization support- The user should be allowed to define new types of objects andconnections. This facility may be used to build some special methodologies. Ideally itshould be possible to specify the rules of a methodology to a rule engine for carrying outthenecessaryconsistencychecks. 207 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) ArchitectureofaCASEenvironment The architecture of a typical modern CASE environment is shown diagrammatically in fig. 39.2.The important components of a modern CASE environment are user interface, tool set, objectmanagement system (OMS), and a repository. Characteristics of a tool set have been discussedearlier. Fig.39.2:Architectureof aModern CASEEnvironment UserInterface The user interface provides a consistent framework for accessing the different tools thus makingit easier for the users to interact with the different tools and reducing the overhead of learninghowthedifferent tools areused. ObjectManagementSystem(OMS)andRepository 208 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) Different case tools represent the software product as a set of entities such as specification,design, text data, project plan, etc. The object management system maps these logical entitiessuch into the underlying storage management system (repository). The commercial relationaldatabasemanagementsystemsaregearedtowardssupportinglargevolumesofinformationstr uctured as simple relatively short records. There are a few types of entities but large number ofinstances.Bycontrast,CASEtoolscreatealargenumberofentityandrelationtypeswith 209 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) perhapsafewinstancesofeach.Thustheobjectmanagementsystemtakescareofappropriatelymappingi nto the underlyingstorage managementsystem. 210 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) 211 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) SOFTWARER EUSE 212 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) Advantagesof softwarereuse Software products are expensive. Software project managers are worried about the high cost ofsoftware developmentand are desperately lookfor waystocutdevelopmentcost.A possibleway to reduce development cost is to reuse parts from previously developed software. In additionto reduced development cost and time, reuse also leads to higher quality of the developedproductssincethereusable components areensured to havehigh quality. Artifactsthatcanbereused It is important to know about the kinds of the artifacts associated with software development thatcan be reused. Almost all artifacts associated with software development, including project planandtest plancan bereused. However, theprominent itemsthat can beeffectivelyreusedare: • Requirementsspecification • Design • Code • Testcases • Knowledge Prosandconsofknowledgereuse Knowledge is the most abstract development artifact that can be reused. Out of all the reuseartifacts i.e. requirements specification, design, code, test cases, reuse of knowledge occursautomatically without any conscious effort in this direction. However, two major difficulties withunplanned reuse of knowledge are that a developer experienced in one type of software productmight be included in a team developing a different type of software. Also, it is difficult toremember the details of the potentially reusable development knowledge. A planned reuse ofknowledge can increase the effectiveness of reuse. For this, the reusable knowledge should besystematically extracted and documented. But, it is usually very difficult to extract and documentreusableknowledge. Easinessof reuseofmathematicalfunctions Theroutinesofmathematicallibrariesarebeingreusedverysuccessfullybyalmosteveryp rogrammer. No one inhis right mind would think of writing a routine to compute sine orcosine. Reuse of commonly used mathematical functions is easy. Several interesting aspectsemerge. Cosine means the same to all. Everyone has clear ideas about what kind argumentshouldcosinetake,thetypeofprocessingtobecarriedoutandtheresultsreturned. Secondly, 213 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal of ISBN : 978-81-963532-2-3 (E-Book) mathematicallibrarieshaveasmallinterface.Forexample,cosinerequiresonlyoneparam eter.Also,thedata formats ofthe parameters arestandardized. Basicissuesinanyreuseprogram Thefollowingaresomeofthebasicissuesthatmustbeclearlyunderstoodforstartinganyreu seprogram. • Componentcreation • Componentindexingandstoring • Componentsearch • Componentunderstanding • Componentadaptation • Repositorymaintenance Component creation- For component creation, the reusable components have to be firstidentified. Selection of the right kind of components having potential for reuse is important.Domainanalysisisa promisingtechniquewhich can beusedtocreatereusablecomponents. ComponentindexingandstoringIndexingrequiresclassificationofthereusablecomponents so that they can be easily searched when looking for a component for reuse. Thecomponents need to be stored in a Relational Database Management System (RDBMS) or anObjectOrientedDatabaseSystem(ODBMS)forefficientaccesswhenthenumberofcompon entsbecomes large. Component searching- The programmers needto searchfor right components matchingtheir requirements in a database of components. To be able to search components efficiently,the programmers require a proper method to describe the components that they are lookingfor. Component understanding- The programmers need a precise and sufficiently completeunderstanding of what the component does to be able to decide whether they can reuse thecomponent. To facilitate understanding, the components should be well documented andshould dosomethingsimple. Component adaptation- Often, the components may need adaptation before they can bereused,sinceaselectedcomponentmay Software Engineering Keerthana P, Manasa KN, Ganga D Bengal notexactly 214

ISBN : 978-81-963532-2-3 (E-Book) fittheproblemathand.However,tinkering with the code is also not a satisfactory solution because this is very likely to be asourceof bugs. RepositorymaintenanceAcomponentrepositoryonceiscreatedrequirescontinuousmaintenance.Newcompo nents,asandwhencreatedhavetobeenteredintotherepository. The faulty components have to be tracked. Further, when new applications emerge, olderapplicationsbecomeobsolete.Inthiscase,theobsoletecomponentsmighthavet oberemovedfrom therepository. DomainAnalysis Theaim of domain analysis is to identifythereusable components foraproblem domain. Reuse domain- A reuse domain is a technically related set of application areas. A body ofinformationisconsideredtobe a problemdomainfor reuse, ifa deepandcomprehensiverelationship exists among the information items as categorized by patterns of similarity amongthe development components of the software product. A reuse domain is shared understanding ofsome community, characterized by concepts, techniques, and terminologies that show somecoherence. Examples of domains are accounting software domain, banking software domain,businesssoftwaredomain,manufacturingautomationsoftwaredomain,telecom municationsoftwaredomain, etc. Just to become familiar with the vocabulary of a domain requires months of interaction with theexperts. Often, one needs to be familiar with a network of related domains for successfullycarryingoutdomainanalysis.Domainanalysisidentifiestheobjects,operati ons,andtherelationships among them. For example, consider the airline reservation system, the reusableobjects can be seats, flights, airports, crew, meal orders, etc. The reusable operations beschedulingaflight,reservingaseat,assigningcrewtoflights,etc.Thedomainanalysisge neralizestheapplicationdomain.Adomainmodeltranscendsspecificapplications.Theco mmoncharacteristics orthesimilarities betweensystems are generalized. 215 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal the can ISBN : 978-81-963532-2-3 (E-Book) During domain analysis, a specific community of software developers gets together to discusscommunity-widesolutions.Analysisoftheapplicationdomainisrequiredtoidentifythereusable components. The actual construction of reusable components for a domain is calleddomainengineering. EvolutionofareusedomainTheultimateresultofdomainanalysisisdevelopmentofproblemorientedlanguages.Theproblemorientedlanguagesarealsoknownasapplicationgenerators.Theseapplicationgenerators ,oncedevelopedformapplicationdevelopmentstandards. The domains slowly develop. As a domain develops, it is distinguishable the variousstagesit undergoes: Stage 1: There is no clear and consistent set of notations. Obviously, no reusable components areavailable.All softwareiswritten from scratch. Stage 2: Here, only experience from similar projects is used in a development effort. This meansthatthereis onlyknowledgereuse. Stage 3: At this stage, the domain is ripe for reuse. The set of concepts are stabilized and thenotations standardized. Standard solutions to standard problems are available. There is bothknowledgeandcomponent reuse. Stage 4: The domain has been fully explored. The software development for the domain can belargely automated. Programs are not written in the traditional sense any more. Programs arewrittenusingadomainspecific language,which isalso knownasan applicationgenerator. 216 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) REUSEAPPROACH ComponentsClassification Components need to be properly classified in order to develop an effective indexing and storagescheme. Hardware reuse has been very successful. Hardware components are classified using amultilevel hierarchy. At the lowest level, the components are described in several forms: naturallanguage description, logic schema, timing information, etc. The higher the level at which acomponentisdescribed,themoreistheambiguity.ThishasmotivatedthePrietoDiaz’sclassificationscheme. Prieto-Diaz’s classificationscheme:Eachcomponent isbestdescribed usinga number ofdifferentcharacteristicsorfacets.For beclassifiedusingthefollowing: 217 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal example,objectscan ISBN : 978-81-963532-2-3 (E-Book) Searching- The domain repository may contain thousands of reuse items. A popular searchtechnique thathasprovedtobe very effectiveisone thatprovidesawebinterface totherepository.Usingsuchawebinterface,onewouldsearchanitemusinganapproximat eautomated search using key words, and then from these results do a browsing using the linksprovided to look up related items. The approximate automated searchlocates products thatappear to fulfill some of the specified requirements. The items located through the approximatesearch serve as a starting point for browsing the repository. These serve as the starting point forbrowsing the repository. The developer may follow links to other products until a sufficientlygood match is found. Browsing is done using the keyword-to-keyword, keyword-to-product, andproduct-toproductlinks.Theselinkshelptolocateadditionalproductsandcomparetheirdetailed attributes. Finding a satisfactorily item from the repository may require several locationsof approximate search followed by browsing. With each iteration, the developer would get abetterunderstandingoftheavailableproductsandtheirdifferences.However,wemustre member that the items to be searched may be components, designs, models, requirements, andevenknowledge. Repository maintenance - Repository maintenance involves entering new items, retiring thoseitems which are no more necessary, and modifying the search attributes of items to improve theeffectiveness of search. The software industry is always trying to implement something that hasnot been quite done before. As patterns requirements emerge, new reusable components areidentified, which may ultimately become more or less the standards. However, as technologyadvances,somecomponentswhicharestillreusable,donotfullyaddressthecu rrentrequirements. On the other hand, restricting reuse to highly mature components, sacrifices one ofthatcreatespotential reuseopportunity.Making a product availablebeforeit hasbeen thoroughly assessed can be counter productive. Negative experiences tend to dissolve the trust in the entirereuseframework. Application generator -The problem- oriented languages are known as application generators.Application generators translate specifications into application programs. The specification isusually written using 4GL. The specification might also in a visual form. Application generatorcanbeappliedsuccessfullytodataprocessingapplication,userinterface,andco mpilerdevelopment. 218 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) Advantagesofapplicationgenerators Application generators have significant advantages over simple parameterized programs. Thebiggest of these is that the application generators can express the variant information in anappropriate language rather than being restricted to function parameters, andthefactthat named ortables.Theotheradvantagesincludefewererrors,easiertomaintain,substantially reduceddevelopmenteffort, theimplementationdetails. oneneednotbotherabout Shortcomingsofapplicationgenerators Application generators are handicapped when it is necessary to support some new concepts orfeatures. Application generators are less successful with the development of applications withcloseinteraction with hardwaresuch as real-time systems. Re-useatorganization level Achievingorganization-level reuserequires adoption ofthefollowingsteps: • Assessingaproduct’spotentialforreuse • Refiningproducts for greater reusability • Enteringtheproduct inthe reuse repository Assessing a product’s potential for reuse. Assessment of components reuse potentialcan be obtained from an analysis of a questionnaire circulated among the developers. Thequestionnaire canbe devisedtoaccessa component’sreusability.Theprogrammersworking in similar application domain can be used to answer the questionnaire about theproduct’s reusability. Depending on the answers given by the programmers, either thecomponentbetakenupforreuseasitis,itismodifiedandrefinedbeforeitisentere dinto the reuse repository, or it is ignored. A sample questionnaire to assess a component’sreusabilityis thefollowing. • Isthecomponent’sfunctionalityrequiredforimplementationofsystemsint hefuture? • Howcommonisthecomponent’sfunctionwithinitsdomain? • Wouldtherebeaduplicationoffunctionswithinthedomainifthecomponent istakenup? • Isthe componenthardwaredependent? • Isthedesignofthecomponentoptimizedenough? • Ifthecomponentisnonreusable,thencanitbedecomposedtoyieldsomereusablecomponents? constants, 219 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) Canweparameterizeanon-reusablecomponent sothat itbecomesreusable? Refining products for greater reusability. For a product to be reusable, it must berelatively easy to adapt it to different contexts. Machine dependency must be abstractedout or localized using data encapsulation techniques. The following refinements may becarriedout: • Name generalization: The names should be general, rather than being directlyrelatedto a specificapplication. • Operation generalization: Operations should be added to make the componentmoregeneral.Also,operationsthataretoospecifictoanapplicatio ncan be removed. • Exception generalization: This involves checking each component to see whichexceptions it might generate. For a general component, several types of exceptionsmighthaveto be handled. • Handlingportabilityproblems:Programstypicallymakesomeassumpti onregardingtherepresentationofinformationintheunderlyingmachine.Thes eassumptions are in general not true for all machines. The programs also often need tocall some operating system functionality and these calls may not be same on allmachines. Also, programs use some function libraries, which may not be available onallhostmachines.Aportabilitysolutiontoovercometheseproblemsisshow ninfig. 41.1. The portability solution suggests that rather than call the operating system andI/O procedures directly, abstract versions of these should be called by the applicationprogram. Also, all platform-related calls should be routed through the portabilityinterface. One problem with this solution is the significant overhead incurred, whichmakes it inapplicable to many real-time systems and applications requiring very fastresponse. 220 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

ISBN : 978-81-963532-2-3 (E-Book) Fig. 41.1:Improvingreusabilityofa component byusingaportabilityinterface Factorsthatinhibitaneffectivereuseprogram In spite of all the shortcomings of the state-of-the-art reuse techniques, it is the experience ofseveral organizations that most of the factors inhibiting an effective reuse program are non-technical.Some ofthesefactors arethefollowing.  Needforcommitmentfromthetopmanagement.  Adequatedocumentationtosupportreuse.  Adequate incentive to reward those who reuse. Both the people contributing new reusablecomponents and those reusing theexisting components should be rewarded to start areuseprogram and keep it going.  Providing access to and information about reusable components. Organizations are oftenhesitanttoprovideanopenaccesstothereuserepository for thefearofthereusecomponentsfinding awayto their competitors. 221 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal ISBN : 978-81-963532-2-3 (E-Book) REFERENCES BIBLIOGRAPHY 1. “FundamentalsofSoftwareEngineering”,RajibMall,Prentice-HallofIndia. WEBLIOGRAPGY 2. http://www.tutorialspoint.com/ 222 Software Engineering Keerthana P, Manasa KN, Ganga D Bengal

/Author details Prof. KEERTHANA P, is an Assistant Professor at Reva University, School of Computer Science and Engineering, Bengaluru, India. She has earned a Bachelor of Engineering in Computer Science and Engineering from Visvesvaraya Technology University. She earned her Master of Engineering in computer science and engineering from REVA University and Currently pursuing a Phd in computer science and engineering at REVA University. She is conducting research in the field of Machine Learning and Deep Learning and Artificial Intelligence. Prof. Manasa KN, is an Assistant Professor at Reva University, School of Computer Science and Engineering, Bengaluru, India. She earned a Bachelor of Engineering in Computer Science and Engineering from Nitte Meenakshi Institute of Technology, Bengaluru, Karnataka in 2012. She earned her Master of Engineering in Information Technology from University Visvesvaraya College of Engineering in 2015. She is conducting research in the field of Image Processing, Computer Vision, Machine Learning and Deep Learning. Prof. Ganga D Benal Technology , Bengaluru, , is an Assistant professor at Cambridge Institute Of India. She earned a Bachelor of Engineering in Information Science and Engineering from Atria Institute of Technology (VTU), Bengaluru, Karnataka in 2019. She earned her Master of Technology in computer science and engineering from REVA University in 2021. She is conducting research in the field of Machine Learning and Deep Learning. ISBN: 978-81-963532-2-3 (E-BOOK) Title: - SOFTWARE ENGINEERING Author: - KEERTHANA P MANASA KN GANGA D BENAL ISBN: - xxxxxxxxxxxxxx Price: 750/

ISBN : - 978-93-88936-10-1 A HANDBOOK ON FMCG SALES Published by International Society for Green, Sustainable Engineering and Management ISO 9001:2015 certified Laboratory Member • Quality Council of India Member • The Indian Science Congress Association • • Indian Statistical Institute Indian Meteorological Society Dr. Raja Roy Choudhury Director Academic Affairs, Universal Business School, India Dr. Chandan Thakur Associate Professor of Marketing , Universal Business School, India [Type text]

[ A HANDBOOK ON FMCG SALES ] CONTENT 1. 2. 2.3. 2.4. 2.5. 3. The FMCG Sales & Distribution Overview Organizational Structure 2.1. Organogram 2.2. Job Description and Personal Profile – SR/TSI/SO Job Description and Personal Profile – ASM Job Description and Personal Profile – RSM Job Description and Personal Profile – NSM Area Planning 3.1. Coverage Plan 3.1.1. Coverage Master Plan 3.1.2. Key Town Master Plan 3.2. PJP(Permanent Journey Plan) 4. 4.1. Field Systems In the Market 4.1.1. Daily Sales Report (DSR) 4.1.2. Retailer Card 4.1.3. Merchandising Kit/Use of POS/Visibility 4.1.4. Planogram 4.1.5. Order Book 4.1.6. ASM Market Visit Report 4.2. At the Distributor 4.2.1. Distributor Monthly Stock & Sales Report (DMSSR) and Inventory Management 4.2.2. Distributor Appointment Process 4.2.3. Distributor Termination process 4.2.4. Claim settlement process 4.2.5. Damage and Expiry Policy 4.2.6. Order Generation Process 4.2.7. Distributor ROI 4.3. At the Super Stockist 4.3.1. Order Generation Process 4.3.2. Inventory Management 4.3.3. Outstanding Management 4.3.4. Super Stockist ROI 1 [ A HANDBOOK ON FMCG SALES ] 5. Meeting and Conferences 5.1. Weekly Meetings Schedule 5.2. Monthly& Quarterly Meetings Schedule and agenda 6. 6.1. Rewards & Recognition Incentives 6.2. Recognitions 2

[ A HANDBOOK ON FMCG SALES ] Abbreviations SR TSI Sales Representative Territory Sales Incharge SO Sales Officer ASM Asst. Sales Manager RSM Regional Sales Manager NSM National Sales Manager DSR Daily Sales Report POS Point of Sales DMSSR Distributor Monthly Stock & Sales Report ROI Return on Investment SKU Stock Keeping Unit PJP Permanent Journey Plan FIFO First in First Out FMFO First Manufactured First Out SO Standard Organogram HQ Head Quarter CFA Carrying Forwarding Agent SD Sales Development MIS Management Information Agent S&D Sales & Distributer CMP Coverage Master Plan KTMP Key Town Master Plan MJP Monthly Journey Plan JC Journey Cycle POSM Point of Sales Material NDC No Dues Certificate HO Head Office 3 [ A HANDBOOK ON FMCG SALES ] POA Power of Authority DOGP Distributer Order Generation Process MSSR Monthly Stock Sales Report SS Secondary Sale OGP Order Generation Process 4

[ A HANDBOOK ON FMCG SALES ] List of Tables Table.2.2.A. Personal profile for SR/TSI/SO Table.2.2.B. Job Description for SR/TSI/SO Table.2.3.A. Personal Profile of ASM Table.2.3.B. Job Description of ASM Table.2.4.A. Personal Profile of RSM Table.2.4.B. Job Description of RSM Table.2.5.A. Personal Profile of NSM Table.2.5.B. Job Description of NSM Table. 3.1.1.A. Coverage Master Plan Table.3.1.2.A. Key Town Master Plan Table.3.1.2.B.Key Towns Summary Table.3.2.A.Permanent Journey Plan Table.3.2.B.Monthly Journey Plan Table. 4.1.1. Typical DSR Table. 4.1.2. Retailer card Table.4.1.5. Order Book Table.4.1.6.A.ASM Market Visit Report Table.4.2.1.A.DMSSR Table.4.2.2.A.Distributor Appointment Table.4.2.4.A.Normal Trade Schemes Table.4.2.4.B.Visibility Schemes Table.4.2.4.C. Sampling Scheme Table.4.2.6.A. Order Generation Process Table.4.2.7.A.Distributor ROI Table.4.3.1.A.Order Generation Process Table.4.3.2.A. Table.4.3.3.A. Inventory Management Super Stockist outstanding monitor 5 [ A HANDBOOK ON FMCG SALES ] Table.4.3.4.A. Super Stockist ROI Table. 5.2.1.A. SKU wise Target Vs. Achievement Table.5.2.2.A. SO Wise / Month Wise Primary Achievement Sheet Table.5.2.3.A.Secondary Achievement Sheet. SO Wise / Month Wise Table.5.2.4.A. Manpower Structure Update Table.5.2.5.A. Distribution Update Table.5.2.6.A. Outlet Coverage Update Table.5.2.7. Inventory & Stock Statement Table. 5.2.8.A. Monthly Summary sheet Table. 5.2.9.A. Next Month Action plan Table.5.2.10.A Sales Forecast Table. 6.1. A. Incentives Table. 6.1.B. Incentives Structure 6

[ A HANDBOOK ON FMCG SALES ] List of Figures Fig.1 Fig.2.1 Standard Organogram Structure of the Supply Chain Fig. 4.1.3 A. Merchandising Kit/Use of POSM/Visibility Fig. 4.1.4.A. Planogram 7 [ A HANDBOOK ON FMCG SALES ] 1. The FMCG Sales & Distribution Overview FMCG or Fast Moving Consumer Goods industry is one of the most penetrated industries in every country. These include items of daily household consumption like cereals, staples, salt, oil, soap etc. The Typical structure of the Supply Chain is as shown below: Procurement Feedback Manufacturing Consumer Company Godown Retailer Super Distributor Wholesale Fig.1 In a country like India, there are 29 States& 6 Union Territories, 640 Districts, 7936 Towns, 641000 villages. The FMCG distribution caters to the entire population directly or indirectly. 2. Organizational Structure 2.1. Organogram The Organogram depicts the Sales Organization at a glance. The hierarchy of Sales start at the frontline with a Sales Representative (SR) or a Sales Officer (SO) 8 Distributor

[ A HANDBOOK ON FMCG SALES ] For every 5 to 10 SO/SR there is one ASM For every 5 to 7 ASMs there is one RSM Typically, in FMCG companies, there are 4-5 regions each headed by RSM. All the RSMs report to a national sales head known as the NSM (National Sales Manager) In addition to the line reporting functions, there are support functions in sales at the regional and national level.These are Sales Development, Trade Marketing and Sales Training. Standard Organogram NSM RSM (East) RSM (West) RSM (North) RSM (South) National Trade Marketin g National Sales Training National Sales Develop ment National Modern Trade Manger ASM 1 ASM 2 ASM 3 Regional Trade Marketing Regional Sales Training Regional Sales Develop ment Regional Modern Trade Manager TSI/SO Fig.2.1 9 [ A HANDBOOK ON FMCG SALES ] 2.2. Job Description and Personal Profile – SR/TSI/SO The SR/TSI/SO is the first line in the Sales Force. These are the people who sell to retailers and hence are the first level of contact between the Retailers and the company. However, in certain companies, the SO is a senior level between the TSI/SR and the ASM. Personal Profile Table.2.2.A 1 Around 20 to 30 years of age 2 Minimum 2 years’ experience with Food FMCG Companies 3 Graduates with diploma in Sales & Marketing 4 Minimum 6 months experience in the proposed geography 5 Thorough knowledge about the market under consideration 6 Fluent in local language 7 Sound Arithmetic Skills 8 Basic Computer Skills 9 Excellent rapport with trade 10 Energetic and enthusiastic 11 Job Description Table.2.2.B S.no Functional area Tasks 1 Market Work Cover 40 shops on a daily basis, as per PJP and beat plan Placing all the SKU in the right quantity in the retail stores Ensure visibility of the products at the stores Ensure FIFO/FMFO at the stores Launching & placing new products as per the company guidelines Maintain relationship with retailers Solving queries of the retailer regarding schemes, returns/expiry etc. 2 Distributor Management Ensure a good relationship with a distributor Managing distributor's inventory and product ageing Help provide healthy ROI to distributors To influence the distributor to provide credit & services in the market 10 Distributor Salesman of A grade companies or Sales Representatives of Mid-Sized Companies to be targeted

[ A HANDBOOK ON FMCG SALES ] S.no Functional area Tasks 3 Market Intelligence 4 Reporting Tracking competition and reporting new developments in the market To prepare and post his DSR Prepare the monthly stock and sales report of the distributors Maintain all sales reports as per company guidelines 2.3 Job Description and Personal Profile – ASM Personal Profile Table.2.3.A 1 Around 30 to 35 years of age 2 Minimum 8-10 years’ experience with Food FMCG Companies 3 Graduates with Diploma/MBA in Sales & Marketing 4 Minimum 2 years’ experience in the proposed geography 5 Thorough knowledge about the market under consideration 6 Fluent in local language 7 Sound Arithmetic Skills 8 Good Computer Skills 9 Excellent people and leadership skills 10 Excellent rapport with trade 11 Energetic and Enthusiastic 12 Sales Officers of A grade companies or ASMs of Mid-Sized Companies to be targeted Job Description Table.2.3.B S.no Functional area Tasks 1 Market Working To ensure availability & visibility in the market Proper Merchandising Identify & solve the problems in the market Checking Coverage vs. Beat Plan and PJP working by the SOs Increase in Productive Outlets per beat Building relationship with Key Outlets Manage Wholesale 11 [ A HANDBOOK ON FMCG SALES ] S.no Functional area 2 Sales Planning & Distribution Enhancement / Excellence Tasks Plan Targets scientifically & Plan to achieve them through various Sources of Distribution Growth Cost Control - Ensure that the operative costs are managed and there is no wastage or over expenditure 3 Team Management 4 CFA/SD 5 Market Intelligence 6 MIS & Reporting Ensure there is no vacant HQ in your area Proper Induction of the New Joinees Supervision of employees and their functioning Employee Motivation Stock inventory management Getting involvement of the CFAs/SDs Setting service standards and implementation of standards Damaged &expired stocks management Understanding the needs of the Market & Opportunities and keeping track of competition Ensure that the required MIS / Reports are sent regularly 2.4 Job Description and Personal Profile – RSM Personal Profile Table.2.4.A 1 Around 35 to 40 years of age 2 Minimum 12 years’ experience with Food FMCG Companies 2 Graduates with diploma in Sales & Marketing 4 Minimum 3 years’ experience in the proposed geography 5 6 7 8 9 Good Computer Skills 10 Energetic and enthusiastic 11 Excellent people and leadership skills 12 Area Manager of A grade companies or RSMs of Mid-Sized Companies to be targeted 12 Thorough knowledge about the market under consideration Fluent in English and local language Conceptually strong in S&D Sound Arithmetic Skills

[ A HANDBOOK ON FMCG SALES ] Job Description Table.2.4.B S. No Functional area Tasks 1 Market Working To ensure availability of all SKUs in the market Plan Proper Merchandising and Visibility Identify & solve the problems in the market Plan Increase in Productive Outlets per beat Building relationship with Key Outlets Wholesale Management Strategy 2 Sales Planning & Distribution Enhancement / Excellence 3 Distributor Management Plan Targets scientifically & Plan to achieve them through various Sources of Distribution Growth Cost Control - Ensure that the operative costs are managed and there is no wastage or over expenditure. Ensure excellent relationship with the Distributors/SDs Increase the “effectiveness of relationship” with the Distributor 4 Team Management Ensure there are no vacancies in your area Proper Induction of the New Joinees Supervision of employees and their functioning Employee Motivation 5 CFA/SD 6 Market Intelligence 7 MIS & Reporting Stock inventory management Getting involvement of the CFAs/SDs Setting service standards and implementation of standards Damaged & Expired Stocks management Understanding the needs of the Market & Opportunities Ensure that the required MIS Reports are received and sent regularly 13 [ A HANDBOOK ON FMCG SALES ] 2.5 Job Description and Personal Profile – NSM Personal Profile Table.2.5.A 1 Around 40 to 45 years of age 2 Minimum 15 years’ experience with Food FMCG Companies 3 Graduates with diploma in Sales & Marketing 4 Minimum 5 years’ experience as head of a region and above 5 6 7 8 9 Thorough knowledge about at least 2 regions Fluent in English and local language Conceptually strong in S&D Sound Analytical Skills Good Computer Skills 10 Energetic and enthusiastic 11 Excellent people and leadership skills 12 Regional Area Manager of A grade companies or NSMs of Mid-Sized Companies to be targeted Job Description Table.2.5.B S.no Functional area 1 Sales Strategy 2 Sales Planning & Distribution Enhancement/Excellence 3 Distributor Management 4 Team Management Tasks Plan and Implement Strategy for continuous growth Plan market Visibility and Trade Marketing Activities Identify & solve problems in the Market Wholesale Management Strategy Plan Targets scientifically & Plan to achieve them through various Sources of Distribution Growth Cost Control - Ensure that the operative costs are managed and there is no wastage or over expenditure. Ensure excellent relationship with the Distributors/SDs Increase the “effectiveness of relationship” with the Distributors Ensure there are no vacant positions of RSM Supervision of RSMs and their functioning 14

[ A HANDBOOK ON FMCG SALES ] S.no Functional area 5 CFA/SD Employee Motivation, rewards and recognition Tasks Stock inventory management Getting involvement of the CFAs/SDs Setting service standards and implementation of standards Damaged & Expired Stocks management 6 Market Intelligence 7 MIS & Reporting Understanding the needs of the Market & Opportunities present Keeping a close tab on competition Ensure that the required MIS Reports are received, analyzed and solutions are implemented 3 Area Planning Area Planning consists of the following: 3.3 Coverage Plan 3.3.1 Coverage Master Plan The Coverage Master Plan gives an overall picture of the area in terms of Super Distributors, Distributors, Outlets, Manpower and Infrastructure. It is important to review the progress on Master Plan from time to time.It also gives an idea of the estimated Sales and Manpower Cost to Sales Ratio. Table. 3.1.1.A S.No SSO HQ town SD AREA 1 Ghaziabad 2 Ghaziabad Meerut 3 Bareilly 4 Bareilly 5 Agra 6 Mainpuri 7 Kanpur 8 Jhansi 9 Varanasi 10 Varanasi 11 Gorakhpur 12 Gorakhpur 13 Lucknow 14 Lucknow 15 Dehradun Bareilly Shahjahanpur Agra Mainpuri Kanpur Jhansi Varanasi Allahabad Gorakhpur Gorakhpur Lucknow Faizabad Dehradun 16 Dehradun Rudrapur No. of Towns Covered Ghaziabad 35 26 22 15 15 20 8 18 16 24 199 No. of Distributors 40 27 23 20 13 17 13 13 21 28 215 Total Cost of two ASMs No of SSO 1 1 1 1 1 1 1 1 1 1 10 No of SR 2 1 2 1 1 1 1 2 2 1 1 1 2 1 1 1 Tentative cost of SSO 15,000 15,000 12,000 10,000 12,000 10,000 12,000 12,000 15,000 14,000 Tentative cost of SR 17,000 7,000 16,000 7,000 8,000 7,000 7,000 14,000 15,000 7,000 7,000 7,000 18,000 7,500 8,000 8,000 Total cost 39,000 38,000 20,000 17,000 19,000 24,000 34,000 26,000 40,500 30,000 Expected Business 50,00,000 50,00,000 25,00,000 25,00,000 25,00,000 37,50,000 50,00,000 37,50,000 50,00,000 37,50,000 21 1,27,000 1,60,500 2,87,500 387,50,000 60,000 3,47,500 232,50,000 1.49% 15 Cost Percentage [ A HANDBOOK ON FMCG SALES ] 3.3.2 Key Town Master Plan The Key Town Master Plan is a detailed geographical plan for each major town in terms of infrastructure and manpower planning. This is illustrated in the Chart Below: Table.3.1.2.A Name of Key Town: Delhi S.No. District/Area Name of Distributor 1 West Delhi RAM ENTERPRISES 2 West Delhi SAI ENTERPRISES 3 North Delhi KOHLI ASSOCIATES 4 North Delhi SHYAM TRADERS 5 South Delhi SETHI BROS. 7 8 Distributor Location UTTAM NAGAR PUNJABI BAGH BURARI MALVIYA NAGAR 6 South Delhi RADHEY ENTERPRISES OKHLA East Delhi ROHAN SALES MAYUR VIHAR East Delhi SHRI KRISHNA ASSOCIATES GEETA COLONY No. of Beats SHALIMAR BAGH 18 14 8 12 20 16 110 12 14 20 14 12 14 22 16 No. of Beats 124 1 1 1 1 1 1 1 1 8 1 1 1 1 1 1 1 1 8 No. of Outlets 400 480 720 560 320 480 800 640 480 560 800 560 480 560 880 640 No. of Sales 4,400 4,960 A summary of all key towns is illustrated in the chart given below: Table.3.1.2.B Town Varanasi Kanpur Agra Aligarh Bareilly District Lucknow Lucknow Gorakhpur Gorakhpur Varanasi Allahabad Allahabad Jhansi Jhansi Kanpur Nagar Agra Aligarh Bareilly Moradabad Moradabad Shahjahanpur Shahjahanpur Meerut Meerut Ghaziabad Ghaziabad Saharanpur Saharanpur Hardwar Hardwar Popu (000's) Expected Outlets Expected Distributors 2765 831 1332 1097 476 2513 1202 719 847 570 330 1346 912 476 Muzaffarnagar Muzaffarnagar 356 Dehradun Dehradun 462 240 1383 416 666 549 238 1257 601 360 424 285 165 673 456 238 178 231 120 3 1 1 1 1 3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 9 1 1 2 1 1 1 2 1 No. of Units 0.5 0.5 0.5 0.5 0.5 0.5 1 Average Sales Current Proposed Current Proposed Current Proposed Current Proposed Current Proposed Current Proposed 10 12 0.5 2,50,000 2,50,000 0.5 2,50,000 2,50,000 1 2,50,000 5,00,000 2,50,000 2,50,000 0.5 10 0.5 5 0.5 2,50,000 2,50,000 0.5 2,50,000 2,50,000 1 5,00,000 5,00,000 0.5 2,50,000 2,50,000 5 22,50,000 25,00,000 16

[ A HANDBOOK ON FMCG SALES ] 3.4 PJP(Permanent Journey Plan) & MJP(Monthly Journey Plan) A PJP is a visitation schedule town wise or beat wise for a Frontline Sales person (SO/SR/SSO). This captures the number of days to be spent on different towns/beats. In a PJP 21 days are fixed and 4 days flexible. The 21 Days also define the sequence of visitation of Towns. The flexible days are kept for emergencies. In addition to this there are 4 Sundays and 1 day for the Monthly JC Meeting. Table.3.2.A Lucknow HQ. SO S.No 3 Gonda 4 Khargupur 5 Maskanwa 6 KureBhar 7 Kadi pur 8 Sultan pur 9 Sidhauli 10 Lucknow 11 Lakhim pur 12 Barabanki 13 Moti Ganj 14 Faizabad 15 Baba Ganj 16 Surepur 17 Koiri pur 18 musafirkhana 19 Biswa 20 Amethi/Gauri G 21 Colonel ganj 22 23 Flexible Days 24 25 26 27 28 29 30 31 JC-MEETING SUNDAY'S Total Days 1 4 30 4 CITY 1 Mehmoodabad 2 Sitapur DAYS OF WORK 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0.5 0.5 S.No 1 Pilibhit 2 Moradabad 3 Budaun 4 Farrukhabad 5 Majna 6 Kaimganj 7 Tilhar 8 Shahjahanpur 9 Kunwar Gaon 10 Thakur dwara 11 Alapur 12 Usaid 13 Usava 14 Bareilly 15 Budaun 16 Kakarala 17 Moradabad 18 Tilhar 19 20 Flexible Days 21 22 23 24 25 26 27 28 29 30 31 JC-MEETING SUNDAY'S Total Days 1 4 30 Based on the PJP, A Monthly Journey Plan or Travel itinerary is made at the beginning of every month. This cannot be changed without the consent of the ASM. This ensures that the markets are visited in an orderly fashion instead of a haphazard way. Bareilly HQ. SO CITY DAYS OF WORK 1 1 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 4 17 [ A HANDBOOK ON FMCG SALES ] Table.3.2.B BAREILY HQ. Date 2 3 4 5 7 9 Day 1 Wednesday Thursday Friday Saturday Sunday 6 Monday Tuesday 8 Wednesday Thursday Friday Saturday Sunday W.Off 10 11 12 13 Monday Tuesday 14 15 Wednesday Thursday Friday 16 17 18 19 21 23 24 25 26 28 30 31 Saturday Sunday 20 Monday Tuesday 22 Wednesday Thursday Friday Saturday Sunday 27 Monday Tuesday 29 Wednesday Thursday Friday W.Off W.Off Working Town EXAMPLE- BAREILY JC-MEETING EXAMPLE- MORADABAD EXAMPLE- BADAUN W.Off Date 2 LUCKNOW HQ. SO Day 1 Monday Tuesday 3 Wednesday Thursday Friday Saturday Sunday 4 5 6 7 8 Monday Tuesday 9 10 Wednesday Thursday Friday 11 12 13 14 16 Saturday Sunday 15 Monday Tuesday 17 Wednesday Thursday Friday 18 19 20 21 23 Saturday Sunday 22 Monday Tuesday 24 Wednesday Thursday Friday 25 26 27 28 30 Saturday Sunday 29 Monday Tuesday 31 Wednesday W.Off W.Off W.Off Working Town EXAMPLE- LUCKNOW JC-MEETING EXAMPLE- Barabanki EXAMPLE- Moti Ganj W.Off 18

[ A HANDBOOK ON FMCG SALES ] 4. Field Systems 4.1 In the Market 4.1.1 Daily Sales Report(DSR) A typical DSR is illustrated in the figure below: Table. 4.1.1. A Name HQ State Distributor Town Worked Beat Worked Starting Time Finishing Time SKUs SKU 1 Today's opening Stock at DB point Today's Target S.No. Name of Outlet Type 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 Today's orders booked Planned Calls Actual Calls Productive Calls % Productivity New Outlets Note: Category A B C D Type Sales Greater than Rs. 1000 Sales Between Rs. 500 to Rs. 999 Sales Between Rs. 100 to Rs. 499 Sales Below Rs. 100 Grocer/Kirana General store Ice Cream/Cold Drink shop Self Service Store Bakers & Confectioners Others GR GS ICS/CDS SSS B & C O A DSR is the primary tool used by a frontline Sales Person in the Market to book Orders. This also is the primary source of Market information for the company. On the one hand, it gives the sales person focus for the day on areas like Total Calls, Productive Calls, Sales Value, Range Selling and the Day Target. 19 Cat. SKU 2 SKU 3 SKU 4 Units Value Date Month/ J.C. Worked with [ A HANDBOOK ON FMCG SALES ] At the same time various parameters mentioned above, can be monitored on a day to day basis by the company management. The DSR data can be summarized into actionable points. 4.1.2 Retailer card A typical Retailer Card is illustrated in the figure below: Table. 4.1.2. A RETAILER CARD MONTH PROPRIETOR NAME: PHONE: A+ A B OUTLET NAME & ADDRESS: GR WEEK 1 S.No. SKU SKU 1 SKU 2 SKU 3 SKU 4 SKU 5 WEEK 2 WEEK 3 CH SM GM B&C SWS WS TOTAL VALUE WEEK 4 OP. STOCK TGT ACH CL.STOCK OP. STOCK TGT ACH CL.STOCK OP. STOCK TGT ACH CL.STOCK OP. STOCK TGT ACH CL.STOCK TGT ACH MARKET: HOLIDAY: C D GR : Grocer SO/SR/ASM SIGNATURE CH : Chemists SM : Super Market GM : General Merchant B&C : Bakery & Confectionery SWS : Semi Whole Seller WS : Whole Seller A retailer card gives us the history of visitation and sales at a particular outlet. This is a very effective tool to monitor our relationship with the Outlet, in terms of visitation frequency and productivity. This is also a good tool to monitor offtake from that particular Outlet and accordingly place the new order. 20

[ A HANDBOOK ON FMCG SALES ] 4.1.3 Merchandising Kit/Use of POSM/Visibility A typical Merchandising Kit is illustrated in the figure below: Fig.4.1.3.A Merchandising Kit consists of tools for creating better visibility in the Market. There are tools like scissors, sketch pens, scotch tape, marker, nails, drawing pens, thread ball, pasting gum, etc. The POSM (Point of Sales Material) normally used consists of posters, danglers, shelf tape, stickers, banners, dispensers etc. The Sales person is expected to carry these to the market and extensively use them for market visibility. Market Visibility is an important way of driving purchase from the outlet. The frontline sales person is responsible for making sure that his products are placed at eye level in the most visible areas of the shop. He is also responsible for implementing schemes like “Shelf Visibility” paid and unpaid. 21 [ A HANDBOOK ON FMCG SALES ] 4.1.4 Planogram A typical Planogram is illustrated in the figure below: Fig.4.1.4.A A planogram is a visual depiction of the way stocks are to be organized in a shop. Normally a planogram is different for Modern and General Trade. The planogram also needs to be in tune with the category. A planogram helps to maintain the range and induce offtake. 4.1.5 Order Book A typical Order Book is illustrated below. Table.4.1.5.A Date: Name of Retailer Name of SO/SR S.no. SKU Avilable Stock Stock Ordered Signature & Stamp 22

[ A HANDBOOK ON FMCG SALES ] The Order Book is needed to record all orders collected in the Market. The order forms are filed in duplicate. The top sheet is signed and stamped by the retailer as a mark of approval. This sheet is then submitted to the distributor for deliveries. The second copy is retained in the Order Book for the Record of the sales person. 4.1.6 ASM Market Visit Report Table.4.1.6.A ASM Name: HQ: Kaju 15/Beat Worked : Worked with SO / DSM Name: NOTE 1- PLEASE TICK THE APPROPIRATE/PLEASE TICK ALL OUTLETS VISITED S.No Name of Retail Oulet Pista 5/1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 TOTAL Observations on distributors working :Observations on SO's working :Competition Activities :23 Noodles 30/Distributor Name Start Time/End Digestives 25/Available Visible Available Visible Available Visible Available Visible [ A HANDBOOK ON FMCG SALES ] 4.2. At the Distributor 4.2.1. Distributor Monthly Stock & Sales Report (DMSSR) and Inventory Management The DMSSR or monthly stock and sales report is a tool to regulate and monitor inventory and observe SKU wise movement of Sales. A typical DMSSR is depicted below. Table.4.2.1.A Distributor Town S.No 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 Total Units Total Value in INR 100 50 82 68 25 2 1 The DMSSR gives us closing stock in units and in number of days. This in turn gives us an idea of which SKUs are over stocked and which SKUs need to be ordered. The DMSSR is used as an input for order generation as well as for clearing old and slow-moving items. This is probably the most important tool to ensure Hygiene at the Distributor point. The DMSSR also gives us details about stock which have returned from the market as damaged/expired or returned by the retailers. SKU Opening Stocks Primary Secondary Sales * Closing Stocks Closing Stock in No. of Days 25 100 50 82 68 Market Return Kaju Saleable Non-Saleable 2 1 24

[ A HANDBOOK ON FMCG SALES ] 4.2.2. Distributor Appointment Process The Distributor appointment is perhaps the most critical area of any FMCG business. The steps to be followed while appointing a distributor are:  The Local TSI or SO to shortlist a few relevant profiles and send them to the ASM for further pruning the list to 2-3.  The ASM to visit all distributors and finally select one, and keep one as backup (provided all parameters are met).  The selected profile to be sent to RSM/NSM/CEO for final approval.  Based on approval, agreement to be signed, a Distributor Code to be created at Head Office and the first payment collected.  Please Note that new distributor can be appointed in place of an existing distributor only after the process of termination is complete and NDC has been collected. A typical distributor profile form is depictedhere: 25 [ A HANDBOOK ON FMCG SALES ] Table.4.2.2.A 1. NAME OF THE FIRM 2. TYPE OF FIRM (PLEASE CIRCLE) 3. ADDRESS 4. NAME OF CONTACT PERSON 5. LANDLINE NUMBER 6. MOBILE NUMBER 7. TIN NUMBER 8. BANK DETAILS SECTION A 1. A. B. 2. A. TYPE OF BUSINESS REDISTRIBUTION OTHERS (ASSIGN SCORE) CURRENT PRODUCT LINE TOP 10 FMCG/FOOD PRODUCT LINES B. OTHER FMCG/Food LINES Please Specify C. OTHERS (ASSIGN SCORE) 3. CURRENT GROUP BUSINESS TURNOVER LESS THAN 5 LAKHS A. B. 5-10 LAKHS C. 10-15 LAKHS D. 15 -20 LAKHS E. > 20 LAKHS 4. FINANCIAL STANDING A. OWN FUNDS - 100% B. OWN FUNDS - >70% C. OWN FUNDS - >50<70% D. OWN FUNDS - >30%<50% E. OWN FUNDS - >30% 5. CURRENT PREMISES OF OPERATION A. OWNED B. RENTED 6. DISTRIBUTION EXPERIENCE A. 01–05 YEARS B. 05 –10 YEARS C. 10 YEARS + 7. MARKETS COVERED A. SAME AS OUR TERRITORY PLAN B. SOME AREAS ARE DIFFERENT C. TOATALLY DIFFERENT AREA SECTION A SCORE OUT OF 35 SECTION B SECTION 1. PROFILE OF THE OPERATING PERSON ( OWNER / PARTNER / MANAGER ) 1. PROFILE OF PROPRIETOR (Rate from 0 - Max Score) A. AGE ( < 35yrs = 3, 35 to 50 yrs. = 2 > 50 yrs. = 0 ) B. TECHNOLOGY SAVVY C. PASSION FOR CURRENT BUSINESS D. CUSTOMER ORIENTED E. RELATIONSHIP WITH OTHER COMPANIES F. BUSINESS ACCUMEN 2. EDUCATION A. PROFESSIONAL/POST GRADUATE B. GRADUATE C. UG 3. COMMUNICATION A. VERY EFFECTIVE B. EFFECTIVE C. NOT EFFECTIVE 4. MANAGING PARTNER'S INVOLVEMENT IN BUSINESS A. 100% OF TIME B. 2-4 HOURS EVERYDAY C. OCCASIONAL REVIEW 5. LOCATION OF OFFICE A. MAIN MARKET B. SECONDARY MARKET C. OUTSIDE TOWN AREA 6. DISTRIBUTION INFRASTRUCTURE A. GODOWN SPACE IN SqFt 1. GREATER THAN 1500 SqFt 2. BETWEEN 500 TO 1500 SqFt 3. LESS THAN 500 SqFt B. NUMBER OF SALESMAN 1. 5 OR MORE 2. 2 TO 4 3. 1 OR BELOW C. NUMBER OF VEHICLES WITH TYPES 1. MECHANISED GREATER THAN 5 2. MECHANISED BETWEEN 2 -4 3. MECHANISED LESS THAN 2 4. NON MECHANISED D. COMPUTERISED ACCOUNTING 1. YES 2. NO SCORE OUT OF 65 FOR SECTION B TOTAL SCORE OUT OF 100 * MECHANISED - THREE WHEELER AND FOUR WHEELER VEHICLES RUNNING ON FUEL * Top 10 FMCG/Food product Line: HUL, NESTLE, DABUR, BRITANNIA, PARLE, ITC, CADBURY, MARICO, GODREJ, PEPSI FOODS COMMENTS : 1. STRENGTHS 2. WEAKNESS 3. SPECIFIC TERMS DISCUSSED Compiled By (SO) MAX. SCORE EVALUATED SCORE 3 3 5 3 3 5 1 2 1 3 2 1 MAX. SCORE EVALUATED SCORE 5 3 1 3 MAX. SCORE EVALUATED SCORE 5 3 0 1 MAX. SCORE EVALUATED SCORE 10 5 8 0 MAX. SCORE EVALUATED SCORE 5 3 1 4 MAX. SCORE EVALUATED SCORE 5 3 2 5 3 1 5 3 1 0 3 1 65.00 100.00 2 MAX. SCORE EVALUATED SCORE 5 1 2 MAX. SCORE EVALUATED SCORE 5 3 1 3 MAX. SCORE EVALUATED SCORE 1 2 3 4 5 3 MAX. SCORE EVALUATED SCORE 5 4 3 2 1 1 MAX. SCORE EVALUATED SCORE 5 2 2 MAX. SCORE EVALUATED SCORE 3 4 5 3 MAX. SCORE EVALUATED SCORE 5 3 0 0 35.00 14 Company Name Turn Over ***ref check mandate Company 1 Company 2 Company 3 SOLE PROPRIETORSHIP PARTNERSHIP PVT. LTD. OTHERS 1 0 1 30 44 Evaluated by (ASM) Approved By (RSM) 26

[ A HANDBOOK ON FMCG SALES ] 4.2.3. Distributor Termination Process Distributors are extremely important for a company and therefore termination of a distributor should be the last resort after giving enough chances to the distributor to improve. The following could be the reasons for terminating a distributor:  Unethical & Fraudulent practices  Lack of interest in business  Poor Finances  Un-hygienic conditions in Godown  No following company policies  Physical assault or abusive language with sales team The process to be followed for terminating a distributor is as follows:  Collect evidence of any of the above and speak to the distributor  If the problem persists collect more evidence and issue a warning letter to the distributor.  If the problem continues, the ASM to meet the distributor and ask him to resign.  Along with the resignation letter get a No Dues Certificate/ Condition No Dues Certificate from the distributor.  In case the distributor does not resign, a suitable termination letter to be issued by the HO invoking the distributor agreement.  The Distributor has to be given enough time to wind up his operations and collect credit from the market.  A new distributor to be appointed only after the expiry of the above period. 4.2.4. Claim Settlement process Schemes are passed in the market in two ways: a) Primary Scheme b) Secondary Scheme The primary schemes are allowed in the invoice itself and hence there is no need to prepare a claim. However, secondary schemes are run by the distributor and later on claimed from the company. The Secondary claims can be of the following nature:  Normal Trade Schemes  Quantity Purchase Schemes  Visibility Schemes 27 [ A HANDBOOK ON FMCG SALES ] Table.4.2.4.A Trade Scheme/QPS BRAND SKU Ketchup TOTAL Table.4.2.4.B Visibility Scheme BRAND Name of Outlet Morvin Stores Butter TOTAL * Please attach photographs which capture sign board Prepared By (SO):  Sampling Table.4.2.4.C Sampling Scheme BRAND Name of Distributor Shyam Traders Butter TOTAL * Please attach photographs which capture sign board Prepared By (SO): Name of Outlet Morvin Stores Quantity Sampled Total Amout Verified By (ASM): Approved By (RSM/NSM/CEO): Discription of Scheme Period Rs. 500 PM for window 14 July-14 Sep Total Amout 1 2 3 4 DISTRIBUTOR LANDING PRICE RETAILER LANDING PRICE M.R.P. Purchase Qty.in Cases Purchase Value Scheme 1 case 1 pack Free 1 case 5 pack Free 1 case 10 pack Free 2 cases 25 pack Free Scheme Scheme Rate/Unit Value in Rs. - - - - Scheme % Verified By (ASM): Approved By (RSM/NSM/CEO): 28

[ A HANDBOOK ON FMCG SALES ]  Other Market Activation(Similar Formats as above) The claims are to be prepared by the SO on the final day of his journey cycle and along with supporting to be submitted to the ASM for verification. The ASM carries all the claims to HO on the monthly meeting date and hands over to accounts for processing. 4.2.5. Damage and Expiry Policy Different companies have different policies for damaged and expired products. However, in the light of a highly competitive market, most companies are forced to be market friendly with respect to it. Food Products in particular are dealt with cautiously by manufacturers. Normally most companies take back any products which have a clear manufacturing defect. The retailers and distributors are compensated fully for such goods. However, in case of transit damage or any other minor damage, the company personnel provide credit notes to the distributors who in turn provide the same to retailer. This is normally subject to a limit and power of authority (POA). For new products which do not sell and expire on the shelves normally reputed companies take back all the unsold stocks and compensate the trade. Some companies give an in-built commission to the distributors to take care of damaged and expired products. Proper processes at distributor point and the market place ensure that there are no damaged or expired stocks in the market. 4.2.6. Order Generation Process As discussed in the DMSSR para, the order of the distributor will depend on the SKU wise closing stock and the secondary sale for the previous period. It will also depend on seasonality, special inputs and competition scenario. Typically, the order should be such that the distributor always has the agreed number of days of stock. For example, in the CFA or SS town it can be 7 days and in an up country town it can be 15 days. It will also depend on the frequency of supplies to the distributor point. The below format illustrates the order process 29 [ A HANDBOOK ON FMCG SALES ] Table.4.2.6.A Distributor Town S.No 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 Total Units Total Value in INR 190 80 187 83 29 30 10.5 40 SKU Kaju Pista Opening Stocks 100 90 Primary Secondary Sales 50 30 82 105 Closing Stocks 68 15 Closing Stock in No. of Days 25 4 Stock Norms (in days) 15 15 Calculated Order Quantity -27 38 Actual Order 0 40 4.2.7. Distributor ROI ROI or Return on Investment is a key parameter to check the health of a Distributor or channel partner. This is a simple ratio of net profit and Total Investment. The chart below illustrates a typical method of calculation of distributor ROI. 30

[ A HANDBOOK ON FMCG SALES ] Table.4.2.7.A ROI Working Assuming Volume of 12000 CBBs Annually servicing 4000 outlets. Average per month volume =12000/12= 1000 CBBs PM Total Revenue Gross Margin(7%) Costs Rent Electricity Unloading Charges One Sales Man One Warehouse Guy One Helper Transport Cost Total Net profit= Gross Margin - Total Cost Net Profit = 12,000 Average Net Profit Per Annum Investment Total Monthly Sales = 1000 CBBs Investment in Stocks(15 days)= 500 CBBs Investment in Credit (21 days)= 700 CBBS Investment in Claims(5% of Total Sales) Total ROI PM= Net Profit/Total Investment *100 = ROI PA= Net Profit/Total Investment *100 = 1,44,000 Monthly 2,000 500 500 0 0 2,000 7,500 12,500 3,50,000 * 1000 CBBs*350 per CBB price 24,500 Annual Cost 24,000 (***Rent - At 5/sqft for 400sqft) 6,000 6,000 (***@ 50 paisa per carton) 0 0 24,000 Shared resource 90,000 (1 Driver + Auto Running Charges) Shared 1,50,000 (Monthly) 350000 1,75,000 2,45,000 17,500 4,37,500 (per month) 2.74% ( Monthly) 32.91% (Annually) Normally ROI of 20% or above is considered satisfactory. However, it is a function of Net Earnings. For example, If the ROI is 50%, but Net Earnings is Rs. 10,000 then the distributor may not be interested, on the other hand if the ROI is 24% and earning is Rs. 2 Lakhs Per Month then the lower ROI is more acceptable. Hence Net Earnings and ROI have to be carefully balanced. 4.3. At the Super Stockist 4.3.1. Order Generation Process As discussed in the Distributor Order Generation Process, the order of the Super Stockist will depend on the SKU wise closing stock and the Sales to Distributors for the previous period. It will also depend on seasonality, special inputs and competition scenario. 31 [ A HANDBOOK ON FMCG SALES ] Typically, the order should be such that the distributor always has the agreed number of days of stock. For example, in the CFA or SS town it can be 7 days and in an up country town it can be 15 days. It will also depend on the frequency of supplies to the SS point. The below format illustrates the order process. Table.4.3.1.A Super Stockist Town S.No 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 Total Units Total Value in INR 190 80 187 83 29 30 10.5 40 SKU Kaju Pista Opening Stocks 100 90 Month Primary Secondary Sales 50 30 82 105 Closing Stocks 68 15 Closing Stock in No. of Days 25 4 Stock Norms (in days) 15 15 Calculated Order Quantity -27 38 Actual Order 0 40 32

[ A HANDBOOK ON FMCG SALES ] 4.3.2. Inventory Management The Super StockistMSSR or monthly stock and sales report is a tool to regulate and monitor inventory and observe SKU wise movement of Sales. A typical SSMSSR is depicted below. Table.4.3.2.A Super Stockist Town S.No 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 Total Units Total Value in INR 100 50 82 68 25 2 1 The SSMSSR gives us closing stock in units and in number of days. This in turn gives us an idea of which SKUs are over stocked and which SKUs need to be ordered. The SSMSSR is used as an input for order generation as well as for clearing old and slow-moving items. This is probably the most important tool to ensure Hygiene at the Distributor point. The SSMSSR also gives us details about stock which have returned from the market as damaged/expired or returned by Distributors. 4.3.3. Outstanding Management One of the key roles of a super stockist is providing credit.However, it is critical that the outstanding of the Super Stockist remains under control. Ideally each distributor should have a negotiated ‘Number of Days of Credit’ Say, from 15 days to 30 days. There should also be a credit risk limit based on previous purchases. While calculating the credit risk limit, cheques in pipeline should also be taken into consideration. 33 Marie SKU Opening Stocks Primary Secondary Sales * Closing Stocks Closing Stock in No. of Days 25 100 50 82 68 Market Return Saleable Non-Saleable 2 1 [ A HANDBOOK ON FMCG SALES ] The format below is a basic format used to monitor outstanding of Super Stockist. Table.4.3.3.A Date: Distributor 1 Distributor 1 Distributor 1 22-12-2013 Pending Invoice No. Pending Invoice Date Pending Invoice Amount Number of Days Credit T502 T503 T504 11-10-2013 02-11-2013 10-12-2013 Total Credit Risk Limit Distributor 2 Distributor 2 Distributor 2 04-10-2013 15-11-2013 20-12-2013 Total 5,000 8,000 20,000 33,000 50,000 7,500 72 50 12 Pending Invoice No. Pending Invoice Date Pending Invoice Amount Number of Days Credit R502 R503 R504 15,000 18,000 40,500 Credit Risk Limit Outstanding Grand Total 50,000 73,500 79 37 2 34

[ A HANDBOOK ON FMCG SALES ] 4.3.4. Super Stockist ROI Table.4.3.4.A ROI Working Assuming Volume of 60000 CBBs Annually servicing 4000 outlets. Average per month volume =60000/12= 5000 CBBs PM Total Revenue(Monthly) Gross Margin(4.5%) Costs Rent Electricity loading + unloading One Sales Man One Warehouse Guy One Helper Transport Cost Total Net profit= Gross Margin - Total Cost Net Profit = 53,250 Net Profit Per Annum Investment Total Monthly Sales = 5000 CBBs Investment in stocks(15 days) Investment in Credit (10 days) Investment in Claims(5% of Total Sales) Total ROI PM= Net Profit/Total Investment *100 = ROI PA= Net Profit/Total Investment *100 = 6,39,000 Monthly 4,000 500 5,000 5,500 4,500 2,000 4,000 25,500 17,50,000 * 5000 CBBs*350 per CBB price 78,750 Annual Cost 48,000 (***Rent - At 5/sqft for 800sqft) 6,000 60,000 (***@ 50 paisa per carton) 66,000 0 24,000 Shared resource 48,000 (***@ 80 paisa per carton) 2,52,000 (Monthly) 1750000 8,75,000 5,83,450 87,500 15,45,950 (per month) 3.44% ( Monthly) 41.33% (Annually) **For Ease of Calculation Average CBB Value has been taken at Rs 350 per CBB 5. Meetings and Conferences 5.1. Weekly Meetings Schedule The ASMs must meet their team once a week, to review the progress against plan. This meeting is also used to review market working and discipline. The key areas to be reviewed are: a. Adherence to MJP b. Total Calls, Productivity and Sales c. Line per Call and Lines per Productive Call d. Achievement Vs. Target 35 [ A HANDBOOK ON FMCG SALES ] 5.2. Monthly & Quarterly Meetings Schedule and agenda The Monthly Meeting is held between ASMs and field force at the end of every month. The objective is to review the previous month and set the agenda for the coming month. After this meeting all the ASMs meet at the HQ to discuss plans with the Senior Management. A JC meeting format contains the following Reports. 1. SKU wise Target Vs. Achievement 2. Primary Achievement Sheet SO Wise and Month Wise 3. Secondary Achievement Sheet SO Wise and Month Wise 4. Manpower Structure Update 5. Distribution Update 6. Outlet Coverage Update 7. Stock Statement and Inventory Tracking SKU Wise and SO Wise 8. Summary Sheet of Month Highlights and Next Month Plan 9. Action Plan for the Next Month 10. Sales Forecast for the next Month All the formats are provided below. However minor modifications can be done as per company’s requirement 5.2.1 SKU wise Target Vs. Achievement Table. 5.2.1.A NAME OF ASM: RAM MONTH : OCT-14. Sl.No SKU ITEMS 1 Kaju 2 Pista 3 Butter 4 Digestives 5 Noodles 6 Soup AREA :JHARKHAND STATE : JHARKHAND Target Achievement Achievement% 1010 566 1222 9895 551 515 13759 0 0% 0% 0% 0% 0% 0% 0% Achievement Same Month Last Year 1200 600 200 5151 545 500 8196 Growth% - - - - - - Achievement Last Month 980 120 11000 1930 0 5 -100% 14035 Growth% -100% -100% -100% -100% - -100% -100% 36

[ A HANDBOOK ON FMCG SALES ] 5.2.2. Primary Achievement Sheet SO Wise and Month Wise Table.5.2.2.A NAME OF ASM: Shyam Singh MONTH : OCT-14. Primary Achievement Sheet 1 Somdutt Sl.No SO/SR Name 2 Gopal 3 Rahul 4 Ram 5 Nitish 6 Self Total Primary. Target Month 1211 1500 1000 454 9000 8500 3000 2514 2500 5537 6500 7000 AREA :JHARKHAND STATE : JHARKHAND % Achieved Achieved Cumulative Target 124% 54545 45% 65650 0% 54555 84% 33665 221% 5454 0% 14000 5.2.3 Secondary Achievement Sheet SO Wise and Month Wise Table.5.2.3.A NAME OF ASM: Shyam Singh MONTH : OCT-14. Secondary Achievement Sheet 1 Somdutt Sl.No SO/SR Name 2 Gopal 3 Rahul 4 Ram 5 Nitish Total 655 554 P.Sced. Target Month 2222 45455 22666 1000 51718 42153 AREA :JHARKHAND STATE : JHARKHAND % Achieved Achieved 750 Cumulative Target 115% 51515 401% 15155 0% 78555 4154 15515 373% 9590 900 Note: Secondary target is 50% of primary target. 5.2.4 Manpower Structure Update Table.5.2.4.A 111% 30000 82% 184815 Cumulative Achieved 10695 12472 27083 24887 6341 81478 % Cumulative Achieved Total Calls 21% 396 82% 467 0% 797 260% 426 21% 480 44% 2566 Productive Calls 209 231 339 212 293 1284 Productive Calls %age 53% 49% 43% 50% 61% 50% Cumulative Achieved 50000 65000 15200 12222 11556 15555 23211 25505 110% 227869 169533 % Cumulative Achieved 92% 99% 0% 36% 212% 111% 74% 37 [ A HANDBOOK ON FMCG SALES ] NAME OF ASM: Shyam Singh MONTH : OCT-14. SL NO Area 1 RANCHI / GUMLA / RAMGARH / GOLA / PATRATU / SIMDANGA / GHAGRA / LARI 2 DALTONGUNJ / GARHWAR /KANDI /CHATTERPUR/ HYDERNAGAR/ JAPLA / PADMA / PANDU 3 DEOGHAR / DUMKA / EAST SINGBHUM / GODDA / JAMTARA 4 TATA /CHAIBASA /CKP /JAMSHEDPUR /SARAIKELA /BARHARWAR /JAMNAGAR /RAJMAHAL /PAKUR /BARHET Total 5.2.5. Distribution Update Table.5.2.5.A NAME OF ASM: Shyam Singh MONTH : OCT-14. S.No SD Area RANCHI 1 2 3 5 GUMLA DALTONGANJ 4 HAZARIBAGH KATRAS 6 RAMGARH TOTAL 5.2.6. Outlet Coverage Update Table.5.2.6.A NAME OF ASM: Shyam Singh MONTH : OCT-14. SL. No DISTRIBUTOR AREA 1 Ranchi 2 Ramgarh 3 Gola 4 Lari 5 Patratu 6 Lachargarh 7 Simdanga 8 Gumla 9 Sisai 10 Ghagara 11 Jaldanga 12 Daltongunj 13 Garhwa 14 Kandi 15 Panki 16 Chaterpur 17 Hydernagar 18 Japla 19 Medininagar 20 Padma 21 Pandu 22 Hazaribagh 23 Barkagaon 24 Ichak 25 Chauparan 26 Galobar 27 Tandwa 28 Chatra 29 Huntergunj 30 Pratappur T O T A L 38 As on 30th APR 2014 AREA :JHARKHAND STATE : JHARKHAND As on Current Month End GROWTH PLANNED DISTRIBUTOR 15 10 5 2 25 30 87 AREA :JHARKHAND STATE : JHARKHAND ACTUAL / ACTIVE DISTRIBUTOR 7 9 1 0 15 25 57 Sanctioned SO Actual SO 10 25 15 5 55 AREA :JHARKHAND STATE : JHARKHAND Gap 15 0 1 1 2 Plan and time line for achievement November '14 October'14

[ A HANDBOOK ON FMCG SALES ] 5.2.7 Stock Statement and Inventory Tracking SKU Wise and SO Wise Table.5.2.7.A NAME OF ASM: Shyam Singh MONTH : OCT-14. Sl.No Variety 1 2 3 4 5 6 7 8 9 Kaju Pista Digestives TOTAL 300 450 5 AREA :JHARKHAND STATE :JHARKHAND SKU Opening Stocks Primary Seconday Sales Closing Stocks No.of days 15/2/1/250 630 0 15/2/1/15/2/1/150 220 5 200 350 10 1690 110 320 40 160 450 0 1960 250 490 5 90 195 25 155 305 5 1520 5.2.8 Summary Sheet of Month Highlights and Next Month Plan Table. 5.2.8.A NAME OF ASM: Shyam Singh MONTH : OCT-14. AREA :JHARKHAND STATE :JHARKHAND HIGHLIGHTS of last month Key Objectives vs Achievements of last month 266 606 5 156 335 20 185 465 3 2041 32 37 30 52 52 24 36 46 18 40 Key objectives for Next Month Competitors Activities CONCERN AREAS Recommendations 39 [ A HANDBOOK ON FMCG SALES ] 5.2.9. Action Plan for the Next Month Table. 5.2.9.A Name : Desg. : Area : Month : SL No Action Points A State Plan 1 B Recruitment 1 C Distributor To be Reactivated/ to be replaced 1 D 1 E 1 F 1 G Merchandising 1 5.2.10. Sales Forecast for the next Month Table.5.2.10.A Month: SKU Last 3 Months Average Plan for the Month Kaju 8000 Data Analysis & Document Cross Checking Creation of POSM Focus on Secondaries Nos To be Done By 40

[ A HANDBOOK ON FMCG SALES ] 6. 6.1. Rewards & Recognition Incentives The sales force should always be incentivized in terms of cash rewards or gifts in order to motivate them to work harder for improving sales. The incentive schemes can be of 2 types a. A Regular Ongoing cash incentive (Monthly/Quarterly) for achieving desired sales results. Typically, there is a full incentive for 100% achievement of Target. For over achieving, the incentive gets multiplied. Normally some companies offer some incentive for achievement between 90% to 100% of the target, an illustration is shown in the table below Table. 6.1. A S.No. Name of SO Target In Volume Achievement Weightage Target In Value Achievement Weightage Final Achievement Final Amount 1 2 3 SO 1 SO 2 SO 3 500 1000 750 480 850 800 50% 50% 50% 10000 15000 20000 9500 14500 21000 50% 50% 50% 96% 91% 106% 5000 0 12000 b. A grand reward Annual/Half Yearly/Quarterly for exceptional performance based on predefined criteria like highest growth, consistent performance, highest sales in a new product, best launch, best visibility etc. An example is given below: Table. 6.1.B Incentive Structure Achievement >95%-99.99% 100% > 100% - 104.99% 105% or more Incentive Amount 50% 5000 6.2. Recognitions In addition to incentives, both at the Sales Force level and Distributor level recognition by senior management is viewed positively. The recognition can be based on pre-defined criteria. This can be in terms of best sales person of the year, distributor with the highest growth etc. 100% 10000 110% 11000 120% 12000 41 [ A HANDBOOK ON FMCG SALES ] FROM THE AUTHORS Dr. Raja Roy Choudhury, Director Academic Affairs, Universal Business School, India is highly qualified in the world of behavioral health and leadership sciences. He holds PhD degrees in Economics and Psychology. He has 33 years of experience in the areas of retail, education, behavioral health and management consulting. Dr. Chandan Thakur, Associate Professor of Marketing at Universal Business School, India, is a Doctorate in Marketing Management and has 22 years of experience in the field of management education, corporate training and marketing research. He has taught at several Business Schools in India as well as abroad. 42

ISBN: - 978-93-88936-09-5 (E-BOOK) RUBBER CULTIVATION IN INDIA: PRODUCTION, DISTRIBUTION & TRADE By Dr. B. FELIX FRANCY & Dr. B.REVATHY Published by International Society for Green, Sustainable Engineering and Management ISO 9001: 2015 certified for R & D, Training An Autonomous Organization under public trust act, Govt of West Bengal, India Laboratory Member Quality Council of India Member the Indian Science Congress Association Member Indian Statistical Institute

RUBBER CULTIVATION IN INDIA : PRODUCTION, DISTRIBUTION & TRADE RUBBER CULTIVATION IN INDIA : PRODUCTION, DISTRIBUTION & TRADE By Dr. B. FELIX FRANCY & Dr. B.REVATHY Publisher’s Name International Society for Green, Sustainable Engineering and Management ISO 9001:2015 certified for Research, Development &Training An Autonomous Organization, Under Public Trust Act, Government of West Bengal, India Laboratory Member Quality Council of India Member The Indian Science Congress Association Indian Statistical Institute 2020

© International Society for Green, Sustainable Engineering and Management Editor in Chief: Dr.Debaprayag Chaudhuri, Chairman Production Editor: Mrs.Soma Das Chaudhuri Published in India by International Society for Green, Sustainable Engineering and Management 94,Garfa Main Road, Ground Floor, Jadavpur, Kolkata-700 075,West Bengal India Mobile:0091 96 74 76 61 80 Email: isgsem.research.kolkata@gmail.com Website: http://isgsemkolkata.blogspot.com Copyright © 2020.All rights reserved. No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, mechanical or photocopying, recording, or otherwise without the prior permission of the publisher. The society names used in this set are for identification purposes only. 1st Edition: April’2020 ISBN : 978-93-88936-09-5 (e-copy only) 450 RUBBER CULTIVATION IN INDIA: PRODUCTION, DISTRIBUTION & TRADE By Dr. B. FELIX FRANCY & Dr. B.REVATHY

Copy Right pag Information contained to be reliable and are c to avoid errors and om brought to the notice o this book. e in this book has been obtained by author (s) orrect to the best of their knowledge. Ever ) from sources believed effort has been made y missions and ensure accuracy. Any error or omission noted may be of the Publisher which shall be taken care of forthcoming edition of However, neither the p any information publ ts responsibility of liabilility for any inconvenience expenses, losses or damage to anyone resulting from conten the publisher ublisher nor the author guarantee the accura blished herein and neither of this book. The author (s) of the b book donot violate an ook have taken all possible care to ensure th y existing copyright or other intellectual p person in any manner whatever. In the event, if the au has been inadvertently writing for corrective a l thor (s) have been unable to track any sourc infringed, the fact may be brought to the n t ce and if any copyright notice of the publisher of India, Ministry of Human Resource Development, Departmen of Higher Education, MHRD as per the app ication having reference number 1019|ISBN|2019|P. ction. The ISBN of e-Book has been generatated by the Government Regards Publisher that the contents of the property rights of any acy or completeness of nor author take any ISBN: 978-93-88936-09-5 RUBBER CULTIVATION IN INDIA : PRODUCTION, DISTRIBUTION & TR Dr. B. FELIX FRANCY , Dr. B.REVATHY RADE RUBBER CULTIVATION IN INDIA : PRODUCTION, DISTRIBUTION & TRADE Dr. B. FELIX FRANCY Assistant Professor in Commerce, St.John’s College, Palayamkottai. & Dr. B.REVATHY Professor and Head, Department of Commerce, Manonmaniam Sundaranar University, Abishekapatti, Tirunelveli-627012, Tamilnadu, India E- mail: revabalamsu@gmail.com Mobile: 94427 81692

ISBN: - 978-93-88936-09-5 CHAPTER I INTRODUCTION AND DESIGN OF THE STUDY 1.1 INTRODUCTION .....................................................................Page 13 1.2 RUBBER MEANING: ..............................................................Page 14 1.3 HEVEA BRASILIENSIS ..........................................................Page 14 1.4 OPERATIONAL DEFINITION ...............................................Page 14 1.4 (A) Small Rubber Growers (SRG) 1.4 (B) Rubber Producers' Societies (RPS) 1.4 (C) supports by co-operatives 1.4 (D)New Planting 1.4 (E)Replantation 1.5 HISTORY AND SCIENTIFIC GROWTH OF NATURAL RUBBER 1.5 (A) Pre-15th Century ...........................................................Page 16 1.6 CONSUMPTION OF RUBBER IN BRITAIN AND AMERICA....Page 19 1.7 SOURCE OF RUBBER ............................................................ Page 20 1.7 (A)Para Rubber (CastillaRubber) 1.7 (B) Ceara Rubber 1.7 (C) Assam Rubber 1.7 (D) Para Rubber 1.7 (E) Guayule Rubber 1.7 (F) Landolphia Rubbers. 1.7 (G) Palay Rubber 1.7 (H) Dandelion Rubber 1.8 USES OF NATURAL RUBBER .............................................. Page 21 1.8 (A) Transportation 1.8 (B) Manufacture of industrial goods 1.8 (C) Communications 1.8 (D) Health care 1.8 (E) Others 1.9 RUBBER GROWING IN INDIA .......................................... Page 23 1.10 MANURING...........................................................................Page 23 1.11 SEASONAL PATTERN IN PRODUCTION ....................... Page 23 1.11 (A) Lean period 2 ISBN: - 978-93-88936-09-5 1.11 (B) Peak period 1.12 STATEMENT OF PROBLEMS .......................................... Page 24 1.13 SCOPE OF THE STUDY ..................................................... Page 25 1.14 OBJECTIVES OF THE STUDY .......................................... Page 25 1.15 METHODOLOGY OF THE STUDY ................................... Page 25 1.15 (A) Sample design 1.16 COLLECTION OF DATA ................................................... Page 26 1.16 (A) Primary data: 1.16 (B) Secondary Data: 1.16 (C) Sources of Secondary Data 1.17 PERIOD OF THE STUDY ................................................... Page 27 1.18 SIGNIFICANCE OF THE STUDY: ..................................... Page 27 1.19 CONSTRUCTION OF TOOLS ............................................ Page 27 1.20 STATISTICAL TOOLS ....................................................... Page 28 1.20 (A) Compound Growth Rate (CGR) 1.20 (B) ANOVA 1.20 (C) Mean Square 1.20 (D) T - Test 1.20 (E) Rank method (Garrett’s ranking technique) 1.20 (F) Chi-Square Test (χ2) 1.20 (G) Correlation co-efficient 1.21 TESTING HYPOTHESIS ...................................................... Page 32 1.22 LIMITATIONS OF THE STUDY ......................................... Page 32 1.23 SCHEME OF THE REPORT ........................................... Page 33 1.24 SUMMARY OF THE CHAPTER ......................................... Page 33 CHAPTER II REVIEW OF LITERATURE 2.1 INTRODUCTION:................................................................ Page 36 2.1(a) REVIEW RELATING TO PRODUCTION AND CONSUMPTION OF THE NATURAL RUBBER 3

ISBN: - 978-93-88936-09-5 2.1 (b) REVIEW RELATING TO NATURAL RUBBER TAPPERS 2.1(c) REVIEW RELATING TO MARKETING OF THE NATURAL RUBBER 2.1 (d) REVIEW RELATING TO RUBBER BASED INDUSTRY 2.2 CONCLUSION FROM THE LITERATURE REVIEW……………. Page 46 2.3 PROFILE OF THE STUDY AREA .......................................... Page 49 2.3 (A) Tamilnadu 2.3 (B) Profile of Kanyakumari District 2.3 (B) 1. Introduction of Kanyakumari District 2.3 (B) 2. Ancient History 2.3 (B) 3. General Information of Kanyakumari District 2.3 (B) 4. Soil Condition 2.3 (B) 5. Agriculture 2.3 (B) 6. Spices and Other Plantation 2.3 (B) 7. Forests 2.3 (B) 8. Temperature 2.4 THE RUBBER BOARD.................................................................... Page 51 2.5 ASSISTANCE FROM THE RUBBER BOARD FOR QUALITY IMPROVEMENT .......................................................................... Page 53 2.6 ARASU RUBBER CORPORATION LIMITED, NAGERCOIL ... Page 53 2.7 SALES IN RUBBER ........................................................................ Page 55 2.8 RUBBER INDUSTRY IN INDIA ................................................... Page 56 2.8(A) Structure of Industry 2.8 (B) Characteristics of the Indian Rubber Industry 2.8(C) Manufacturing Process in Rubber Industry 2.8(D) Machinery Manufacturing 2.8(F) Rubber Chemicals 2.9 RUBBER RESEARCH INSTITUTIONS ..................................... Page 58 2.10 KONAM LATEX INDUSTRIES PRIVATE LIMITED (KLIP) ..... Page 58 2.11 SUMMARY ...................................................................................... Page 58 4 ISBN: - 978-93-88936-09-5 CHAPTER III NATURAL RUBBER PRODUCTION AND CONSUMPTION 3.1 INTRODUCTION............................................................................. Page 60 3.2 NATURAL RUBBER ....................................................................... Page 61 3.3 CULTIVATION ................................................................................ Page 61 3.4 METHODS OF CULTIVATION …………………………………. Page 61 3.4.1 Preparation of land: 3.4.2 Planting of rubber trees: 3. 4.3 Manuring of rubber trees 3.4.4 Intercropping: 3. 4.5 Dry Natural Process (DNP) 3.4.6 Natural Rubber Latex Process (NRL) 3.4.7 Collection of Rubber (LATEX) 3. 4.8 Yield Cycle of Rubber 3. 4.9 Marketing of rubber 3. 4.10 Sale through Retailers 3.5 FERTILIZERS USED FOR CULTIVATION ...................................... Page 64 3.6 VARIETY OF RUBBER ....................................................................... Page 64 3.7 EXPENSES INCURRED IN PRODUCTION FOR 1KG OF SHEET...Page 64 3.8 IMPORTANCE OF NATURAL RUBBER ...........................................Page 64 3.9 NATURAL RUBBER – MAJOR END –USES....................................Page 65 3.10 PRODUCTION SECTOR...................................................................... Page 65 3.11 WORLD RUBBER SCEANERIO:....................................................... Page 66 3.12 GLOBAL INDUSTRIAL DEMAND FOR RUBBER ........................ Page 66 3.13 RUBBER CULTIVATION IN INDIA ................................................ Page 76 3.13 (A) Origin of Natural Rubber in India 3.13 (B) Early commercial plantations in India 3.14 INDIAN RUBBER................................................................................ Page 78 3.15 STATE‐WISE PRODUCTION OF NATURAL RUBBER.................. Page 80 3.15.1 Traditional Regions: 3.15.2 Non- traditional region: 3.16 STATE-WISE AREA AND PRODUCTION OF NATURAL RUBBER IN INDIA ....................................................................................................... Page 80 3.17 RUBBER CULTIVATION IN TAMILNADU................................... Page 81 5

ISBN: - 978-93-88936-09-5 3.18 RUBBER CULTIVATION IN KANYAKUMARI DISTRICT ...... Page 81 3.19 SUMMARY ..................................................................................... Page 99 CHAPTER IV SOCIO-ECONOMIC STATUS OF NATURAL RUBBER TAPPERS IN KANIYAKUMARI DISTRICT 4.1 INTRODUCTION............................................................................. Page 101 4.2 WHO ARE TAPPERS? .................................................................... Page 101 4.3 MEANING AND DETAILS OF TAPPING .................................... Page 101 4.4 IMPORTANCE OF TAPPING …………………………………… Page 102 4.5 OPERATIONAL TERMS IN TAPPING ........................................ Page 103 4 .5 (A) Bark 4 .5 (B) Marking, slope and direction of tapping cut 4 .5 (C) Standard of tapping and height of opening 4.5 (D) Tapping depths, bark consumption and bark renewal 4 .5 (E) Time of tapping, task and utensils 4 .5 (F) Tapping systems 4 .5 (G) Rain guarding 4 .5 (H) Tapping rest 4 .5 (I) Rubber Tappers Bank (RTB) 4 .5 (J) Latex 4 .5 (K) Latex flow 4 .5 (L) Properties of Rubber 4 .5 (M) Processing of the Crop 4 .5 (N) Smoking 4 .5 (O) Drying of rubber sheets 4 .5 (P) Grading 4 .5 (Q) Packing 4.6 EVOLUTION OF TAPPING ........................................................... Page 108 4.7 EXPORT OF RUBBER PRODUCTS ............................................. Page 108 4.8 SUMMARY ...................................................................................... Page 127 6 ISBN: - 978-93-88936-09-5 CHAPTER V MARKETING OF RUBBER IN KANYAKUMARI DISTRICT 5.1 INTRODUCTION ............................................................................... Page 129 5.2 OPERATIONAL TERMS USED IN MARKETING OF RUBBER.... Page 130 5.2 (A) Retailers 5.2 (B) Dealers 5.2 (C) Wholesalers 5.2 (D) Co-operative Marketing 5.2 (E) Marketable Forms of Natural Rubber 5.2 (F) Brokerage and commission 5.2 (G) Fluctuations in Rubber Price 5.2 (H) Inadequate Supply of Rubber 5.2 (I) Price of rubber 5.2 (J) Economic importance of Rubber 5.2 (K) Rubber wood 5.2 (L) Physical properties of Rubber Wood 5.2 (M) Consumption of Rubber Wood. 5.2 (N) Marketing Channel 5.2 (O) Branding 5.3 INDIAN RUBBER MARKET ............................................................. Page 135 5.4 CHARACTERISTICS OF NATURAL RUBBER MARKET IN INDIA… Page 135 5.5 PROBLEMS FACED BY SMALL GROWERS IN MARKETING… Page 136 5.6 INTERNATIONAL RUBBER SCENARIO ...................................... Page 139 5.7 IMPORT OF NATURAL RUBBER:.................................................. Page 139 5.8 MARKETING AVAILABLE FOR RUBBER PRODUCER 5.9 SUMMARY CHAPTER VI -FINDINGS AND SUGGESTIONS OF THE STUDY 6.1 INTRODUCTION ..............................................................................Page 155 6.2 MAJOR FINDINGS OF THE STUDY ……………………… ….. Page 155 6.2 (A) Findings related to secondary data 6.2 (B) Findings related to cultivation or growers: 6.2 (C) Findings related to marketers/distributors: 6.2 (D) Findings related to NR Tappers: 6.3 SUGGESTIONS OF THE STUDY………………………………… Page 162 7

ISBN: - 978-93-88936-09-5 6.3 (A) Suggestions related to production 6.3(B) Suggestions related to tapping 6.3 (C) Suggestions related to women tappers 6.3 (D) Suggestions related to the Government 6.3 (E) Suggestions related to marketing 6.3 (F) Suggestion related to Rubber Board 6.4 CONCLUSION……………………………………………………… Page 167 Table No Tables 1.1 1.2 1.3 2.1 2.2 2.3 3.1 3.2 3.3 3.4 3.5 .6 3.7 3.8 3.9 3.10 3.11 3.12 3.13 3.14 3.15 3.16 3.17 3.18 Consumption of Rubber Source of Rubber State wise distribution of Area and number of units from 1955-56 to 1970-71. Field Rubber Production Rubber Production Grade Factory Production Expenses required for cultivation World level rubber production by various countries from 2001 to 2015 World Rubber Consumption Consumption of Natural rubber in main producing countries in the world: Type-wise Production of Natural Rubber in India Consumption of Rubber – Sector wise – product –wise: Production and Consumption Trend of NR Total Area, Production and Productivity in(kg)yield/ha Distribution of Sample Respondent by Age Distribution of Sample Respondent by Sex Distribution of Sample Respondent by Literacy Distribution of Sample Respondent by Marital Status Distribution of Sample Respondent by Categories Distribution of Sample Respondent by using Type of Fertilizer Distribution of Sample Respondent by the Preference for NR Cultivation Distribution of Sample Respondent by Kinds of intercropping Distribution of Sample Respondent by Mode of Cultivation Distribution of Sample Respondent by Quality of Natural Rubber 8 ISBN: - 978-93-88936-09-5 Table No 3.19 3.20 3.21 3.22 3.23 3.24 3.25 4. 1 4. 2 4. 3 4. 4 4. 5 4. 6 4. 7 4. 8 4. 9 4. 10 4. 11 4. 12 4. 13 4. 14 4. 15 4. 16 4.17 4. 18 4. 19 4. 20 5.1 5.2 5.3 5.4 Tables Distribution of Sample Respondent by Methods of Drying Distribution of Sample Respondent for Tapping the Rubber Trees Distribution of Sample Respondent by using Type of Vessels Distribution of Sample Respondent for the factors affecting production Co-efficient of Regression, for Age and sources of production Education and Sources of NR production by growers Chi-square test for sources of NR cultivation Total Area, Tapped Area, Production and Average Yield per Hectare of Rubber Distribution of sample respondents by Mode of tapping Level of Satisfaction of Tappers Factors of NR tappers in Kanyakumari district Paired sample test used for Natural Rubber Tapper Paired sample t- test used for Natural Rubber Tapper The Paired-Samples correlation test Sources of income earned Mode of tapping Payment received by the tappers Mode of repayment by the tappers Function of the government support Problems faced by the tappers in tapping Advance received by the tappers Training attended by the tappers Government support to the tappers Nature of assistance received from the Rubber Board Year of experience Amount of monthly income Satisfaction of tappers with dependent variable (One way ANOVA) Physical properties of Rubber Wood Production, Import, Export and Consumption of Natural Rubber Foreign Trade of Rubber Products in India Annual Average Rubber Price (Rs. 100 kg) in Domestic Market 9

ISBN: - 978-93-88936-09-5 Table No 5.5 5.6 5.7 5.8 5.9 5.10 5.11 5.12 5.13 5.14 5.15 5.16 5.17 5.18 5.19 5.20 Tables Month-wise export of Natural Rubber Country-wise Export of Natural Rubber Channel of distribution Sources of finance Problems in Storage Problems in Marketing Reasons for Rubber Sheets sale through Commission agents Problems in Transportation (Marketing) Problems in marketing cost for Rubber sheet Reasons for Rubber sheets sales through commission agents Factors for getting information about price of rubber sheets Maintenance cost of rubber sheet Reasons for rubber sheets sales through wholesales Problems faced by the distributors of rubber sheets Socio economic status of NR distributors in Kanyakumari district Paired sample correlation 10 ISBN: - 978-93-88936-09-5 LIST OF ABBREVIATIONS ANOVA ANRPC ARCL ARGMA ATMA CAGR CARD CUT F O B GMS Ha. IISRP INRO IRSG ISO ITRO Kg K.K Dist. LFT LPG MT NABARD NR NRL NS OSHA RIS RPIS RPS RR RRII RSS RTB S SHGs SMG SR TIDC TN UNCTAD UPASI : Analysis of Variance : The Association of Natural Rubber Producing countries : Arasu Rubber Corporation Limited : ASEAN Rubber Glove Manufacturers Association India’s Automotive Tyre Manufacturers Association : : Compound Annual Growth Rate : Cultural Academy for Rural Development : Controlled Upward Tapping : Free On Board : Great Mekong Sub-Region : Hectare : : : : : International Institute of Synthetic Rubber Producers International Natural Rubber Organisation International Rubber Study Group International Standards Organisation International Tripartite Rubber Organisation : kilo gram : Kanyakumari District : Low Frequency Tapping : Liberalisation, Privation, and Globalisation : Metric Tonne : National Bank for Agricultural Rural Development : Natural Rubber : Natural Rubber Latex Process : Not significance : Occupational Safety Health and Administration : Rubber Information System : Runner Production Incentive Scheme : Rubber Producers Societies : Reclaimed Rubber : Rubber Research Institute : Ribbed Smoked sheets : Rubber Tappers Bank : Significance ; Self Help Groups : Standard Malaysian Glove Scheme : Synthetic Rubber : Tripura Industrial Development Corporation : Tamil Nadu : United Nations Conference on Trade and Development : The United Planters’ Association of Southern India 11

ISBN: - 978-93-88936-09-5 WTO : World Trade Organisation 12 ISBN: - 978-93-88936-09-5 CHAPTER - I 1.1 INTRODUCTION Rubber plays an important role in the industrial and the economic development of the country. Rubber plantations provide the principal raw material required for the manufacturer of around 35,000 rubber products, ranging from toy balloons to tyres of giant earthmoving equipment. India is the fourth largest consumer of natural rubber. Rubber was introduced to America first. Then it was introduced to Asian and African countries in 1876. In this condition, particularly in Kerala and Kanyakumari District, rubber has been cultivated for nearly 110 years. Now in India, rubber is cultivated in 711560 ha, area and is produced from 477230 ha, area. Approximately, 1806 kg rubber is produced for one hectare area. Among this production 90 per cent of rubber is produced in Kerala alone. In Kanyakumari District, rubber is cultivated in more than 25,000 ha area, in rubber production, related works, factories, vendors and exporters are involved, directly or indirectly. Rubber has been responsible for the life of more than one crore people. Rubber is the main ingredient in the production of gloves, slippers, balloon, tyre, rubber band, rubber ball, thin rubber thread and even the artificial heart valves. Statistical data reveals that at the world level, more than 40,000 articles of a great utility value are produced from rubber. Rubber is being cultivated and produced in various countries like Malaysia, Thailand, Indonesia, Sri Lanka, Singapore, China, Vietnam, Philippines, Cambodia, Nigeria and Brazil. But in India, quality rubber is being produced from Kashia village, which is located at the southern tip of Kanyakumari district. In the present scenario, the development of automobile business and the growth of other types of business have increased the need of rubber. For the past few years, farmers have been very happy about the demand for rubber. The low production of synthetic rubber is due to the hike in the price of petroleum products and also the price of rubber. Rubber trees are widely grown in the states of Kerala and the adjoining Kanyakumari District of Tamil Nadu. These are the traditional rubber growing areas of the country. Both the areas are geographically and agro-climatically suitable for rubber cultivation. Besides this, rubber is also grown in Tripura, Assam, Meghalaya, Mizoram, and other north-eastern states. In India rubber plantations spread over an area of 5.78 lakhs hectares in 16 states across the country. The production of rubber is dominated by smallholdings which account for 91 per cent of the total production and 88 per cent of the area with an average holding size 13

ISBN: - 978-93-88936-09-5 of 0.5 hectare. Nearly 0.7 million people are engaged in these rubber plantations as workforce either directly or indirectly. 1.2 RUBBER: Rubber is found in the fluid of some specific plants but it can also be produced synthetically. Synthetic rubber is produced through the process of polymerization of various monomers. Natural rubber is produced by the process of tapping of the plant called Hevea Brasiliensis. The rubber tree is a native of the Amazon River basin in South America. The ideal rubber growing regions should be 8 degree North of Equator, 10 degree South of Equator, and having a high temperature, an altitude not beyond 400m and a high humidity. These plants generally have 32 years of economic life but they may live up to 100 years or even more than that. The plantation would start its yield from 6th year onwards. The natural rubber produced is processed to convert into a storable and marketable form. In India, the peak season for harvesting rubber is from October to January, while the lean period is during monsoon season. The basic property of rubber is that it comes back to its original shape if it is twisted or stretched but it heat is applied to the rubber, it won’t return to its original shape easily. 1.3. HEVEA BRASILIENSIS Hevea brasiliensis is the most important commercial source of natural rubber. It is a product extracted from its latex. Natural rubber, however, has been found in the latex of over 2000 species of plants belonging to 311 genera of 79 families. The rubber tree is sturdy, quick- growing and tall. It grows on many types of soils, provided they are deep and well drained. A warm, humid, equable climate (21C to 35C) and a fairly distributed annual rainfall of not less than 200 cm are necessary for the optimum growth. The rubber trees have well developed tap root and lateral. The bark, on tapping, yields latex. 1.4 OPERATIONAL DEFINITIONS 1.4 (A) Small Rubber Growers (SRG) Farmers or land owners, cultivating rubber are called rubber growers. The term 'small rubber growers' has been traditionally used to refer to an agriculturist who manages a farm that is relatively small in size. In India rubbers holding of 20 hectares and below are called 14 ISBN: - 978-93-88936-09-5 smallholdings. But the average size of a smallholding is less than one hectare. Perhaps, India is the only country where the average size of rubber holding is so small. According to Rubber Plantation Development Scheme -phases I to IV (1980-2000) the term 'small rubber grower' stipulates that the total area planted with rubber owned by a person or jointly by a group of persons should have its extent limited to 5 hectares. According to the Rubber Act, 1947, small grower means, "an owner whose estate does not exceed 50 acres in area (20.33 hectares)." But a vast majority of the members of the Rubber Producers' Societies are owners of rubber area up to 5 hectares. Therefore, small rubber growers for the purpose of this study include rubber growers having rubber area between 50 cents and 5 hectares. A rubber smallholding in India is statutorily defined as a holding that does not exceed 20 hectares. According to him, the average size of a smallholding is 1.19 hectares. 1.4 (B) Rubber Producers' Societies (RPS) The Rubber Producers' Societies are grass root level voluntary organizations of small rubber growers registered as charitable societies and promoted by the Rubber Board. RPSs are now functioning as extension arms of the board. They are actively involved in the implementation of almost all farmers support schemes. The services rendered by RPS to the growers in areas of technology transfer, input supply, plantation maintenance, harvesting practices, primary processing and quality improvement of sheets, environment protection through proper effluent treatment and rubber marketing are laudable. 1.4 (C) supports by co-operatives Co-operatives institutions at the village, taluk, district and the state levels have played a major role in supporting rubber development activities. Through timely delivery of plantation inputs at reasonable price, these institutions have assisted in increasing rubber production an ensured higher value for the money spent by the farmers. The role played by the co-operatives in marketing rubber is commendable. When rubber price fell to the rock bottom and there were no takers for rubber produced by the small and marginal growers, certain co-operatives dared to purchase their rubber at the minimum notified price even suffering financial loss. It was a great relief to the farmers. 1.4 (D) New Planting There cannot be any increase in new planting especially in the small holding sector unless rubber cultivation becomes financially attractive in the long run. Under the existing market conditions, there is a tendency among small holders to switch over to other crops. This can be checked only by effecting appropriate price policy measures for natural rubber. 15

ISBN: - 978-93-88936-09-5 1.4 (E) Replantation Replanting for encouraging replantation of old and uneconomic areas with high yielding varieties of rubber, the growers must be given incentives such as the supply of highly subsidized fertilizers, subsidies for controlling leaf fall disease and for manuring during the immaturity period. 1.5 HISTORY AND SCIENTIFIC GROWTH OF NATURAL RUBBER NR is found in the lattices of over 2,000 species of plants belonging to 311 genera of 79 families. In 1826, Michael Faraday discovered that natural rubber has five carbon atoms in the repeat unit. Rubber was an existence millions of years ago. Columbus during his second visit to South America during 1493-96, also reported the use of the material as a “bouncing ball for playing games” by the inhabitants of Haiti. Rubber once was an ordinary forest tree in Amazon forest of America but now has been changed and remained as a cash crop for 150 years. Heavia Brazilians is an important type of rubber which has not only been cultivated everywhere but also been known for its good quality and high yield. There are also some other types of rubber which have not been preferred for cultivation because of its low yield. Let us briefly discuss about the life history and various scientific development and its uses of natural rubber. 1.5 (A) Pre-15th Century Rubber is used by Latin American Indians who collect latex from rubber trees and dry it on stick over smoky flames. 1496 : Christopher Columbus introduces rubber to Europe. During his second expedition to the New World, he finds natives of West Indies playing with bouncing balls, collects a few of them and presents them to Queen Isabella of Spain. 1530 : Peter Martyr publishes his bookde Orbe Novo, which has the earliest reference to rubber. 1703 : C.Francis Fresneau gives the first general description of the method of tapping and preparing crude natural rubber (NR). 1745 : Charles de la Condamine makes a report to the Paris Academy of Sciences for used to make water-proof fabrics, shoes, elastic bottles, bracelets, bells and balls, by natives of the Amazon valley. 1755 : Several pairs of boots of King Joseph of Portugal are sent report to Brazilian port of Para for coating with rubber latex. 16 ISBN: - 978-93-88936-09-5 1763 : P.I Macquer and L.A.M Herrisant discover that rubber dissolves in either and turpentine. 1770 : Edward Nairne, a shopkeeper in Britain, starts selling small cubes of rubber as erasers, the first commercial use of rubber, then known as elastic gum. 1775 : Joseph Priestly, famous English chemist, gives the name ‘rubber’ to elastic gum noticing its ability to rub off pencil marks. 1779 : The Portuguese attempts, at Para, to manufacture rubber goods including surgical appliances. 1791 : A Fourcroy finds that ammonia preserves NR 1810 : Roxburgh in Calcutta gets a gift of honey from Assam, in a receptacle lined with Focus rubber. 1818 : James Syme proves naphtha is an efficient rubber solvent. 1820 : Thomas Hancock discovers mastication and invents hand-driven wooden masticator. 1823 : Charles Macintosh uses naphtha rubber solution as a water-proofing layer between fabrics and develops the ‘Macintosh’ water proofing process, which he patents. 1826 : Thomas Hancock invents the rubber board. 1836 : E.M. Chaffee invents calendar to make uniformity thick rubber sheet without using solvents. 1839 : Charles Goodyear discovers the process of vulcanization. 1845 : H. Bewley inverts extruder. 1846 : Alexander Parkes patents the use of carbon disulphide as rubber solvent for making water-proof garments. 1853 : Spensor patents railway springs, to ‘cushion’ concussion. 1860 : Charles Hanson Greville Williams, British chemist, discovers that by destructive distillation, isoprene, the building block of NR, can be obtained. 1876 : Henry Wickham’s collection of seeds, are sent to the Botanical Gardens in Ceylon. 1879 : Bouchardt succeeds in decomposing NR into isoprene and in polymerising in back into rubber. 1882 : Sir William synthesises rubber from isoprene. 1888 : John Boyd Dunlop of Scotland independently inverts the first useful pneumatic tyre, making possible the new era of motor cars. 1890 : William Barlett invents tyre bead, which makes possible tyre detachment from the rim. 1895 : Michelin brothers complete the Paris car race in a tyres-fitted vehicle. 17

ISBN: - 978-93-88936-09-5 1896 : H.J.Doughty develops the first tyre curing press. 1904 : S.C Mote of the Indian Rubber Gutta and Telegraph Works in England 1906 : George Oenslager discovers organic accelerators for reducing vulcansisation time and the chemicals. 1909 : German chemist Fritz Hofmann patents development of poly-isoprene synthetic rubber (SR). 1910 : Dunlop Co., makes the first motor car tyre. 1916 : Fernley. H. Invents the internal mixer which revolutionises rubber mixing/compounding. 1917 : The first truck tyre is made. 1920 : Untied develops the first latex foam rubber. 1922 : Thermal process for making carbon black is introduced. 1923 : Utermark discovers processing of latex concentrates by centrifuging.1924: Herbert. A. Winkelmann and Harold Gray develop the first commercially feasible antioxidants, to reduce rubber’s degradation from exposure to oxygen, ozone and ultraviolet radiation. 1925 : The Rubber Research Institute of Malaya, the premier institution devoted to research on NR in the world, comes into being. 1930 : Du Pont commercialises the SR, duprene, the polymeric structure of which is similar to NR. It is renamed neoprene in 1936. 1937 : Dunlop Rubber Co., sets up the Ist Indian tyre factory in Bengal. 1947 : Rubber is used first in roads on Rue Ferrier in Geneva. The Rubber (Production and Marketing) Act comes into force in India and the Indian Rubber Board is constituted under it. 1952 :The first liquid rubber, ‘Rubbone’, is commercially made.NR bridge bearings made. 1960 : IISRP (the International Institute of Synthetic Rubber Producers) comes into being. 1961 : A new family of SRs consisting mainly of 3 types called ‘stereo regular’ emerges. 1964 : The International Standards Organisation (ISO) releases the draft standards for the technical grading and presentation of NR. 1974 : First death due to latex allergy reactions is reported from US. 1975 : The Rubber Board of India releases the high-yield (2500kg/ha) RRII 105 for commercial planting. 18 ISBN: - 978-93-88936-09-5 1980 : The International Natural Rubber Organisation formed by NR producing and consuming countries, come into being to operate a buffer stock scheme to prevent violent fluctuations in NR prices. 1995 : the Occupational Safety Health and Administration (OSHA) urge healthcare facilities to address the latex allergy issue, generating a large market for medical gloves that do not induce allergy. 1998 : The Standard Malaysian Glove Scheme (SMG) is launched to offer NR latex gloves processed to standards prescribed in the US, Europe and the Asia/Pacific. 2001 : Thailand, Malaysia and Indonesia organise ITRO (International Tripartite Rubber Organisation) to ensure remunerative price for NR. Also, at the initiative of the three, ARGMA (ASEAN Rubber Glove Manufacturers Association) is founded to take steps to secure a fair price for NR gloves. 2002 : China emerges as the top rubber (both SR and NR) consumer with 3060800 tonnes, displacing the US, which consumes only 3006000 tonnes in a year. 1.6. CONSUMPTION OF RUBBER IN BRITAIN AND AMERICA NR is a high performance industrial raw material. It is indispensable in many industrial uses, and accordingly there is a strong relationship between demand and level of economic activity. Rubber products are part of our daily life. Rubber occupied a place in history even before Columbus. The following table explain the consumption of rubber before 19th century by Britain and America. Table 1.1 Year 1830 1840 1850 1860 1870 1880 1890 1900 Consumption of Rubber (in tonnes) Britain 23 307 385 2152 7656 8479 13200 25644 America NA NA NA 750 4296 8109 15336 22026 Source: Rubber Research Institute of India – Kottayam. From the above table it can be seen that the consumption of rubber increased considerably. In Britain, it increased from 23 tonnes in 1830 to 25644 tonnes in 1900. Similarly, the consumption in America had up to 22026 tonnes in 1900 from 750 tonnes in 1860. 19

ISBN: - 978-93-88936-09-5 1.7. SOURCES OF RUBBER Rubber is the latex obtained from several climbers and trees of flowering plants belonging to families Moraceae, Euphorbiaceae and Apocynaceae. Table 1.2 Sources of Rubber Common name Para rubber Ceara rubber Panama rubber Assam rubber Indian rubber Landolphia rubber Palay rubber Guayale rubber Dandelion rubber Botanical name Hevea brasiliensis Manihot heptaphylla Castilla elastic Ficul elastic Fiscus Krishna Landolphia kirki Source: Rubber Asia Jan.1999. 1.7 (A) Para rubber (Castilla rubber) This is obtaining from Castilla elastic of the family Moraceae. The plant is a native of Mexico and Central America. Trees which are 8-10 years old yield of good quality latex. 1.7 (B) Ceara rubber Also called Manitoba rubber, this is obtained from species of Manihot. These are grown in many tropical countries including India. 1.7 (C) Assam rubber The rubber tree is a native of Indo-Malayan region. The tree is found in northern India from outer Himalayas to Nepal and Assam, Khasi hills etc. A most and warm climate found in the forests of Assam is most suitable for the growth of these plants. 1.7 (D) Para rubber This tree is growing in plantations of South India. Rubber obtain from this plant is not good as para rubber. Stems, to a large extent and roots to a lesser extent are tapped to obtain the latex. Latex is collected usually from trees which are 20 years or more old. The rubber is not as elastic as para rubber due to a high resin content. 1.7 (E) Guayule rubbers Parthenium argentatum of the family Asteraceae is the source of this rubber. The plant is found in United States of America. 1.7 (F) Landolphia rubber Rubber is obtained from landolphia kirkii, belonging to the family Apocyanaceae. 1.7 (G) Palay rubber 20 Moraceae Moraceae Moraceae Apocyanaceae Cryptostegia grandiflora Apocyanaceae Parthenium argentatum Asteraceae Taraxacum koksaghza Asteraceae Family Euphorbiaceac Euphorbiaceac ISBN: - 978-93-88936-09-5 Members of Apocyanaceae like Cryptostegia grandiflora is the source of palay rubber. The plants are native of Madgascar near the African coast. 1.7 (H) Dandelion rubber The dandelion plant (taraxacum koksaghza) has tuberous roots, from which latex is obtained for the manufacture of rubber. 1.8. USES OF NATURAL RUBBER Natural Rubber is a versatile material used in the manufacture of more than 50000 varieties of products ranging from toy balloons to giant tyres produced in the country. It is obtained from rubber plantations and natural rubber is also used for precision and it could be used in a wide array of applications with synthetic rubbers. Because of its elasticity, resilience and toughness, natural rubber is the basic constituent of many products used in the transportation, industrial, consumer, hygienic and medical sectors. Of these major end-use markets for rubber, transportation is by far the largest single sector, with tyres and tyre products accounting alone for over 50 percent of Natural Rubber consumption. Truck and bus tyres would represent the largest single outlet for Natural Rubber, followed by automobile tyres. In General, rubber goods are used for commercial and industrial purposes. These nontyre rubber items include industrial products for example, transmission and elevator belts, hoses and tubes, industrial lining, and bridge bearings. Consumer products (included golf of football balls and other recreational and sports goods, erasers, footwear and other apparel); Articles for use in the medical and health sector are, condoms, catheters and surgical gloves) as well as seismic materials. For instance, over 500 and 2500 building are respective fitted with seismic rubber bearing in China and Japan? Latex articles such as gloves, threads, adhesives and moulded foams could be included in different categories in terms of end-use. Rubber is a yellowish, elastic, amorphous material obtained from the latex or milky sap of various tropical plants like the rubber tree. This latex is vulcanized, pigmented, finished and modified into various products like electronic insulation, elastic bands, tyres, hoses, gaskets and containers. Rubber is also known by its scientific name “caoutchouc” or “India rubber”. 1.8 (A) Transportation Rubber is an essential element for all forms of modern transportation. It is used for making tyres, tubes, engine mountings, brakes, radiator, hoses, oil seals, beadings, matting’s, linings, cushions etc., necessary for the automobile industry. Rubber is indispensable in other 21

ISBN: - 978-93-88936-09-5 forms of transportation like bicycles, ships, animal drawn vehicles, hand carts, railways, aeroplanes etc. 1.8 (B) Manufacture of industrial goods Rubber plays a significant role in the manufacture of industrial goods such as belts, packings, moulded goods, and hoses. 1.8 (C) Communications Rubber plays a vital role in communication and transmissions, mainly in the form of insulation for wires and cables. 1.8 (D) Health care In health care and family planning rubber plays an important role in making catheters, hospital sheeting’s, dipped goods, like surgical glove, examinations glove, condoms and a host of other products indispensable in patient care. 1.8 (E) Others Large quantum of rubber is used in making foorwear, proofed fabrics, sheets, floorings, mats and mattresses which are all essential for the day to day life of the people. GEOGRAPHICAL DISTRIBUTION NR cultivation has been traditionally confined to a narrow belt extending from Kanyakumari district of Tamilnadu in the South to Dakshin Kannada and Kodagu districts of Karnataka. Later on, NR activities were extended to non-traditional regions. The total NR acreage is only about 0.57 million ha, there are over one million growers in the country, including 300 estates each with over 20 ha. In other words, NR cultivation in India is essentially a small holder’s affair. The following table shows the State wise distribution of Area under rubber at the end of each year from 1955-56 to 1970-71. Table 1.3 State wise distribution of Area and number of units from 1955-56 to 1970-71. Tamilnadu Kerala Year No. of units 1955-56 27133 1956-57 35473 1957-58 43783 1958-59 50005 1959-60 54822 1960-61 57260 1961-62 62832 1962-63 66206 Area in ha.s 78457 88879 99874 109518 116732 122628 132840 137713 No. of units 81 120 184 255 361 437 587 724 22 Area in ha.s 3773 4187 4393 4687 5111 5508 6238 6576 Karnataka No. of units 16 18 19 21 23 23 27 31 Area in ha.s 1503 1578 1589 1594 1598 1598 1631 1689 ISBN: - 978-93-88936-09-5 1963-64 69874 1964-65 70917 1965-66 75026 1966-67 81015 1967-68 91802 1968-69 98344 1969-70 105112 1970-71 108917 144208 146151 154878 161074 169650 174734 182528 187762 856 896 1073 1249 1487 1626 2206 2463 6804 7223 7823 8082 8352 9248 9846 10015 36 36 36 66 89 112 145 188 1755 1755 1755 1844 3160 3102 3740 4708 Source: Asian Rubber Handbook and Directory 2005 The above table shows that Kerala alone contributes 89 per cent of the total rubber produced in India and an area of 187762 ha in 1970-71 under rubber plantations. Tamilnadu contributes second position of the total NR production. During the period, from 1955-56 to 1970-71, rubber cultivation in India has been traditionally increased both number of units and area. 1.9. RUBBER GROWING IN INDIA Rubber cultivation in India has traditionally been confined to a narrow belt extending from Kanyakumari district of Tamil Nadu in the South to Dakshin Kannada and Kodagu districts of Karnataka in the north and lying general west of the Western Ghats. Later it was extended to non-traditional regions including North East India, Goa, Konkan region of Maharashtra, parts of Odissha, Andhra Pradesh, and West Bengal, where rubber is now being grown. In recent years among non-traditional regions, Tripura has become one of the most thrust areas for Rubber growing because of its good reception worldwide. In fact, Tripura was declared the “Second Rubber Capital of India” by the Rubber Board. India has succeeded in rubber cultivation due to research and extension work undertaken by the Rubber Board. The worldwide demand for NR from Tripura is mainly because of its elasticity. The NorthEastern region contributes up to 5 per cent of the total population, while Karnataka contributes 3 per cent. An area of 113685 ha is covered by rubber plantations in the northeastern region. 1.10MANURING The majority of our rubber growing soils belong to the laterite and lateritic types with only little variations in the inherent fertility status. Hence the following general manorial recommendations are given for rubber of different age groups growing in typical lateritic soils. The fertilizer requirements of rubber vary considerably during the three important stages of growth, namely, nursery, immature and mature. 1.11. SEASONAL PATTERNS IN PRODUCTION 23

ISBN: - 978-93-88936-09-5 The Production of NR in India is characterized by lean and peak periods on the basis of changes in climatic conditions in the major rubber producing centres. 1.11 (A) Lean period Lean periods are those spans of period in which production is at the lowest due to adverse climatic conditions. There are two lean periods, Feb - March and June – July. During Feb, and March, the low yield is due to high temperature. The Majority of rubber growers give rest to rubber trees. When monsoon is in progress during June and July, rubber tapping becomes difficult and production comes down. It is clear that the yield is the lowest even though rain guard is used for tapping during rainy season. In Kanyakumari district several growers do not use rain guards for continued tapping during rainy days. Rainfall is an important factor that affects NR production and productivity in the district. Kanyakumari district alone loses an average of 27 tapping days every year due to heavy downpour. The average production loss per tapping day is about 91 tonnes and 2457 tonnes every year which is 11 per cent of the total population in the district. If rubber growers in the district use rain guard, rubber production can increase by 11 percent p.a. 1.11 (B) Peak period The yield of NR reaches the highest level during the peak period with the most favourable climatic conditions. May and October to January are the two peak periods. 1.12. STATEMENT OF THE PROBLEM Rubber is one of the export products which generate revenue to the country. Today rubber is used for many purposes. It has an economic value. In, Kanyakumari district rubber cultivation is the prime activity. Nearly 80 percent of the people are engaged in rubber production and marketing. Rubber is the major cash crop, in this area. It enhances the regular income and brings foreign exchange and provides employment opportunities. The aim of study is to analyse the problems and the suggestions of rubber producer and marketers and to come out with possible practical suggestions. The questions that arise are as follows: a) What are the problems faced by the rubber cultivators and marketers? b) What are the socio-economic conditions of rubber producers? c) What are the reasons for the varying quality and quantity of rubber, produced in different seasons? d) How do the growers they get finance for rubber cultivation? 24 ISBN: - 978-93-88936-09-5 Kanyakumari district of Tamilnadu provides the best suitable climatic conditions for planting rubber. The rainfall pattern, temperature range, wind speed and the other prevailing facilities are favourable for planting rubber. 1.13. SCOPE OF THE STUDY The present study has been made to examine the socio-economic conditions of the rubber tappers, rubber producers and the marketers of the rubber products. The study further identifies the factors influencing the rubber production and marketing and also examines the quality and quantity of rubber production in various seasons. This study further discusses the availability of finance, utilization of finance and problems faced by the rubber producer and distributors of rubber. 1. 14. OBJECTIVES OF THE STUDY 1. To study the factors influencing the production and marketing of rubber in Kanyakumari District. 2. To trace the marketing conditions of rubber in Kanyakumari District. 3. To analyse the problems that are faced by the rubber cultivators in the marketing of products in Kanyakumari District. 4. To examine the socio-economic conditions of rubber tappers. 5. To discuss the problems faced by the rubber tappers in Kanyakumari District. 6. To offer suggestions to increase productivity of rubber products and to enhance the marketability of rubber. 1.15. METHODOLOGY OF THE STUDY Designing of a suitable methodology and selection of effective analytical tools are important for a meaningful analysis of any research problem. This section is devoted to a description of the methodology which includes sample design, collection of data and tools of analysis. 1.15 (A) Sample design The sample comprises natural rubber growers, distributors and rubber tappers in Kanyakumari District. In Kanyakumari district, there are 12 Rubber markets available for the rubber growers. In Tamilnadu rubber and clove are cultivated only in Kanyakumari District. The quality of rubber produced in Kanyakumari is one of the best in the world and the yield per acre is also very high compared to other parts of Tamilnadu. In Kanyakumari District, natural rubber is grown in about 35000 hectares and the estimated annual production is about 25000 tonnes. Rubber plantations are located in the northern part of the taluks namely Kalkulam, 25

ISBN: - 978-93-88936-09-5 Vilavancode and Thovalai. The total numbers of samples were 550 from rubber producers, tappers, wholesalers and retailers by adopting purposive sampling. From the total sample of the study, 300 respondents are selected among the rubber producers (cultivators), 150 respondents are selected among rubber tappers, and 150 respondents are selected among distributors. Thus, equal importance is given to the production and marketing of natural rubber. 1.16. COLLECTION OF DATA Both primary and secondary data are used for the present study. The primary data are collected from the sample respondents by questionnaire method with well-designed and structured short questions. Before understanding the main survey, a tentative questionnaire is prepared and administered to the natural rubber growers and distributors in order to test the validity of the questionnaire. It facilitated the removal of the ‘non-response’ and unwarranted questions and thus the modified final schedule are prepared. 1.16(A) Primary data: The selected respondents are contacted in person and the objectives of the study are explained to them and their co-operation is ensured. A well-structured questionnaire is distributed to all the respondents. Direct personal interviews are arranged with the rubber growers, distributors, tappers, employees of rubber board and rubber industrialists to elicit essential information pertaining to the study. 1.16(B) Secondary Data: Following are the secondary data used for the study. i) Production, consumption, export, import, and stock of NR from 2001-02 to 201415. ii) World Level Rubber production by various countries from 2001 to 2015. iii) World rubber consumption from 2001-02 to 2014-15. iv) Consumption of NR in main producing countries in the world from 2001-02 to 2014-15. v) Type-wise production of NR in India 2001-02 to 2014-15. vi) Month wise export of Natural Rubber 2001-02 to 2012-13. 1.16(C) Sources of Secondary Data The following are the main sources from which the secondary data required for the present study were collected. Indian Rubber Statistics, Rubber Board Bulletin and Rubber Statistical News (Rubber Board, Kottayam), Planter's Chronicle (UPASI, Connoor), Rubber News (Polymer Publication, Mumbai), Parliamentary Digest (Business Information Bureau, 26 ISBN: - 978-93-88936-09-5 New Delhi), Rubber Statistical Bulletin (International Rubber Study Group, Bangkok), Hand Book of Rubber Statistics (All India Rubber Industries Association, Mumbai) and Rubber Asia ( Dhanam Publications, Cochin). The secondary data related to the study are also collected from journals, newspapers, magazines, books, dissertations, e-journals, websites and relevant official records. Adequate information related to rubber plant, methods, preservation, collection of milky rubber, functions of rubber board and process are obtained from renowned books referred from several libraries. Further, news papers such as The New Indian Express, The Hindu Business Line, Daily Thanthi, and The Hindu (Tamil and English) and journals like Rubber Growers Guide, Economic and Political Weekly, Southern Economist, Kissan World, Rubber Agro Management Diary, were the other sources of secondary data collected for the present study. The strenuous attempts have been made in collecting reviews of literature pertinent to the study from various dissertations, journals, conference proceedings and reports. 1.17PERIOD OF THE STUDY The field survey was conducted from August 2011 to March 2013. The reference period of the survey was from 2001 to 2015. 1.18SIGNIFICANCE OF THE STUDY: Rubber plantation in India is dominated by smallholdings. Although the State of Kerela continued to dominate in the country’s supply of NR, recent years have seen a gradual shift in favour of Non-traditional regions especially some districts of Karnataka and NorthEastern States. Natural Rubber is available in good quality and hence there is a huge scope for setting up of rubber based industries. Rising demand, steep rise in the price of SR and reduced supply from some previously dominate rubber-producing countries have contributed to a sharp rising trend of the price of NR over the last decade. Unfortunately, in the current year the prices of the NR in India have reduced drastically due to the cheap availability of import rubber. In India, the scope of further area expansion in traditional areas being limited, there has been some effort to extend rubber plantation to non-traditional areas, such as Karnataka, Maharashtra, Goa, Andhra Pradesh in the Southern part and Tripura, Assam and Meghalaya in the North-Eastern part of the country. 1.19CONSTRUCTION OF TOOLS The questionnaire prepared was used in this study. Before preparing the questionnaire, experts were contacted and discussions were done with rubber tappers, producers and marketers. The prepared questionnaire has been tested through the selected sample 27

ISBN: - 978-93-88936-09-5 respondents. Further, an in-depth analysis was done with the support of the questionnaire prepared for final study. 1.20STATISTICAL TOOLS To make the present research study highly effective, meaningful and fruitful, the following statistical techniques were used for collection, analysis and interpretation of data. A detailed and well prepared questionnaire used for this study and an interview method is adopted to collect necessary data to carry out this project. For data analysis tables and diagrams are being used extensively which facilitate the calculations of averages, percentages, standard deviation, correlation and coefficient of variations where they are necessary. In order to study the relationship between domestic price of NR and the variables such as production, consumption, international price, import and exported., . The following tools for this study like tables, charts, Spearman co-efficient of correlation , Chi-Square Test, Pair sample test, Ftest and T- test analysis, Liker’s five points scaling methods are used for various problems faced by rubber cultivators and marketing. 1.20(A) Compound Growth Rate (CGR) Compound growth rate is calculated for analysing the growth of production of rubber, import, export and consumption of natural rubber. The growth rate is calculated as below. Yt = Yo (Hg)t ABt where Ya=A and (Hg) = B Yt = ABt Taking log, both sides Log Yt = log A + t log B Ie Y* = A* + t B* When log Yt = Y* log A = A and log B = B* This is simple regression line in Y* and t B* can be estimated using least squares method. Then the estimate of compound growth rate can be obtained as ^ ^ g (Anti log B)-1). For expressing the compound growth rate in percentage terms, ^ has to be multiplied by 100. That is g 100 ^ (Anti log ^) -1) ×100 B g 28 ISBN: - 978-93-88936-09-5 AB y t* −   ) Y t * = 2 t  −  1.20(B) ANOVA ANOVA test is used to find the significant differences existing among the three or  ( t) n 2 more sample groups in relation to a variable. The total variance in a set of data is divided into variation within groups and variation between groups. The ANOVA technique is based on the concept of sum of squared deviations from a mean. Corresponding to the total variance and its two components, we have the total sum of squares (SS), between groups sum of squares (SSb), within groups of squares (SSw) is obtained by combining the sum squares i.e., the squared deviations of every raw score from its sample mean. The formula used is SSw = ∑d2 + ∑d2 + ∑d2 + ∑d2 + ∑d2 + …………………∑d2 1 2 3 4 5 n Where d = a deviation of every raw score of a category from its sample mean. Between groups sum of squares (SSb)is by calculating the difference between each sample mean and the total mean. The squared difference is multiplied by the sample size in the concerned category and these quantities. The formula is SSb= ∑[(x-x1)2 ×n] Where, X = any sample mean X1= the total mean n = the number of scores in any sample SSb = the between groups sum of squares The total sum of squares (SS1) is equal to a sum of within and between groups sum of squares. SS1 = SSb+ SSw 1.20(C) Mean Square The value of the sum of squares tends to become larger as variation increases and also as sample size increases. The mean square (or variance) is obtained by dividing SSb or SSw by the appropriate degrees of freedom. 29 ( *)( n

ISBN: - 978-93-88936-09-5 MSb = SSb/d MSw = SSw/dfw fb Where, MS = the be MSw = the w df = dfb = Where, k = the numb n = the total number of scores in all samples combined. er of samples (groups) 1.20(D) T - Test calculated among two va iables. respect to a variable. It is r Theoretical work on t-dist statistic” is defined as: x t = −µ S Where, S = x n  ( −x x2 n−1 distribution as: v f t C= + ( ) 1   Where, t = C = a constant required to m v = n-1, the number of degr To test the signific r t = 1 − r 2 x n − 2 2 30 ake the area under the curve equal to unity. ees of freedom. an ce of the correlation coefficient the followin g formula is used: t2   v  − +2 2 2 ) The t-distribution is derived mathematically under the assu mption of a normal ‘t’ test is used to study the significant differences among two gr is also used to test In the study for ribution was done by W.S. Gosset in the the significance of a co the latter purpose groups of samples with correlation co-efficient e‘t’ test is employed. e early 1900. The “tthe k-1 tween- groups mean squares ithin – group mean squares dfw = n1-k e degrees of freedom k ISBN: - 978-93-88936-09-5 Where, t is based on (n-2) d If the calculated val 5% level. If t < egrees of freedom. alue of t exceeds 1.20(E) Rank method (G To find out the prob for (n-2), d.f., the value of r is significant at the data are consistent with the hypothesis of an unc Garrett’s ranking technique) blems those are faced by the cultivators for tra d to rank the problems in transportation. Th and points were given in the following order. As per this method, re ranking technique was used asked to assign the rank for into score value with the help of the following formula: 100( 0.5) Percent position = Where, Rij = Rank given fo Nj = Number of variable ranked by j’the respondents r the i’th variable by j’th respondents With the help of Garrett’s Table, o the percent position estima scores. Then for each fact r, the scores of each individual are added a scores and mean values o considered to be the most im 1.20(F) Chi-Square Test (χ The significant dif fe expenses, sales and profit (χ2) 2 (χ2 f score is calculated. The factors having h portant factor. ) ated is converted into and then total value of highest mean value is R −i j Nj ncorrelated population. all factors and the outcomes of such ranking ransportation. Garrett’s he ranks were assigned respondents have been ng have been converted with the factor size of holding in studied through chi-square text ntity of scrap rubber, rent between quantity of rubber sheets, qua (χ2 ) (0 − E ) E O=observed frequen E=Expected frequencies cies The expected frequencies are calculated as follows ( E = -Row total t R X ct Gt ) -Coloum total-Grand total 31

ISBN: - 978-93-88936-09-5 1.20 (G) Correlation co-efficient Pearson’s product moment correlation co-efficient is calculated to find the relationship between production and consumption of NR, import and export etc., ∑XY = ∑x/ ∑X2X ∑Y2 Where, X = x x− Y = y y− x = mean of x variable y =mean of y variable 1.21TESTING HYPOTHESIS • The production, tapping area and the price of natural rubber in Kanyakumari district will be on the rise from 2001-2002 to 2014-15 showing a positive correlation. • There is positive relationship existing between rainfall and productivity. The tapping affected by rain is more due to south west monsoon season in Kanyakumari district. • The domestic price of NR has been influenced by its production, consumption, import, international price, and imports and exports. • There is no positive correlation among the Rubber distributors in the three taluks under study as regards the problems they face in carrying out their operations. • There is a relationship between education and sources of NR growers in Kanyakumari district. • There is no significance between Sex wise classification of tappers and Government Support to the NR tappers in Kanyakumari district 1.22LIMITATIONS OF THE STUDY The study takes into account only the production and marketing of natural rubber in Kanyakumari district. Therefore, the present study has certain limitations. They are, (i) The Non-availability of proper secondary data and records that were a great obstacle in this study. (ii) The result of the primary data duly depended upon the trustworthiness of the respondents. (iii) The scope of the study is limited as it pertains to only Kanyakumari district. (iv) The data are collected only from 150 rubber tappers, 300 producers (growers) and 150 marketers. (v) As all the growers were unable to express the details of market surplus in common unit of measurement i.e., tonnes, they were to be converted. In the process, the exact figures might not have been reported due to approximations 32 ISBN: - 978-93-88936-09-5 1.23. SCHEME OF THE REPORT The broad framework of the study is organized into the following chapters scheme. The present study “PRODUCTION AND MARKETING OF RUBBER WITH REFERENCE TO KANYAKUMARI DISTRICT” is divided into six chapters. The first chapter is inclusive of the Brief Introduction, History of natural rubber, uses of rubber, objectives of the study, hypothesis frame, period of the study, operational definition of concepts, methodology of the study, tools of analysis, limitations of the study and scheme of the report. The second chapter has undertaken extensive searches in journals, newspapers, magazines, books, dissertations, e-journals, websites and relevant official records. This chapter is devoted to the Review of Previous Studies and past literature. It include the profile of Kanyakumari District, the performance of the rubber board and the functions of the rubber industry The third chapter deals with the, Production and Consumption of natural rubber, world position of rubber, rubber cultivation in India, rubber cultivation in Tamilnadu, and socio-economic status of small NR growers in Kanyakumari District, and the process of natural rubber into rubber sheet. The fourth chapter presents operational definition of tapping, methods of tapping, analyses the Socio-Economic Status of NR tappers in Kanyakumari District. The fifth chapter deals with Marketing of natural rubber, international and domestic price of rubber sheets, import and export of rubber sheets, and analyses the socio-economic status of marketers (distributors) in Kanyakumari District The sixth chapter “summary of findings, suggestions, and conclusion” presents the major findings of the study, conclusion and suggestions based on the findings. 1.24SUMMARY • NR is considered as strategic because its consumption may be treated as an index of economic progress. Though it is produced in the poor countries major portion is consumed by rich countries. • In India rubber cultivation is on a narrow belt in the western coast extending from Kanyakumari district of Tamil Nadu state in south to Kodagu district of Karnataka state in the north. This area is often referred to as traditional area. Now cultivation 33

ISBN: - 978-93-88936-09-5 of rubber is also spread to non-traditional areas in states of Maharastra, Andhra Pradesh, Orissa and West Bengal. • A product remained in an unprocessed or partly processed state is called primary product. NR is primary though some sort of processing is done to produce smoked rubber sheets. This is because the processing does not change the essential characteristics of rubber. • The natural rubber from the inception to the present period has been produced under the directions and control of various governments. In the early period it was collected by imperial states like Spain, France, and England. Later its production and exchange was under the control of state governments. However the interests of all these efforts were to gain maximum surplus for the capital employed in rubber related business. 34 ISBN: - 978-93-88936-09-5 35

ISBN: - 978-93-88936-09-5 CHAPTER -II REVIEW OF LITERATURE 2.1 INTRODUCTION: This study is primarily concerned with the production and marketing of rubber. To carry on this study along with the scientific lines by choosing an appropriate research problem the investigator proposes to review some of the outstanding research publications available on the subject. Rubber plantation industry is noted for its ramifications. It has been found to be fertile a venue for explorative studies. As an employment, potential industry, stimulating a chain of production and marketing activities all over the country it has been studied. There is already a plethora of details available and hence efforts are made here to give a fitting introduction to them as a prelude for evolving a suitable research problem for the study. A review of the existing literature was found highly useful in designing the present study. A brief account of some of the relevant studies made previously is given under the four heads. a. Production of Natural Rubber b. Natural rubber tappers c. Marketing of Natural Rubber d. Rubber based industry. 2.1(a) REVIEW RELATING TO PRODUCTION AND CONSUMPTION OF THE NATURAL RUBBER (1) Tariff Inquiry Commission (1967), reports that, “Costs and Prices of Raw Rubber “the price of natural rubber has been maintaining an uptrend. It threatens to assume very grave dimensions with the passing of time; the commission feels that this problem could be got over by a careful introduction of highly scientific methods of rubber cultivation and rubber processing. (2) Bai Leela Baby (1990), in his study titled, “Location and Organization of Small Scale Enterprises in Kanyakumari District”, opines that, Tamil Nadu stands second place in the rubber cultivation in India. Kanyakumari District is the only Rubber production district in Tamil Nadu. There are 63 rubber based industries in Kanyakumari District. (3) Gangadharan made his study about the subsidy schemes introduced in countries all over the world to boost up the area under rubber cultivation. According to him 36 ISBN: - 978-93-88936-09-5 countries like Indonesia, Ceylon, Nigeria, Liberia and Philippines provided subsidies that have led to considerable increase in the area under rubber during 1956 to 1991.He has further pointed out that the India this scheme could be interested to pave the way for growth of the area under subdivision and fragmentations are a major hurdle for the growth of the rubber plantations in India. He wants that the government to adapt strict plantations to stop this case. (4) Economics survey 2009-10 stated that “India is the fourth largest producer of natural rubber with as 8.9 per cent share in world production in 2008. The small holding sector accounted for 89 percent of rubber planted area and 93 per cent of NR production deposits not having reasons geographically best suited to growing NR. India continued to record the highest productivity in the world with an average yield of 1,867 a Kg/ha productivity is further being improved through the rubber plantation development schemes in the 115 years plan. The schemes provide subsidy on planting supply of circle inputs with price concession, assistance for soil and water conservations and generation and distribution of quality planting materials. (5) The Hindu business line was stated that the rubber board gives financial assistance for acquiring computer and peripherals to facilitate access to information technology (IT) enabled services, under rubber plantation development scheme the assistance is limited to Rs.45, 000 per RPS or 75 per cent of the actual cost of the computer and peripherals RSP, functioning in their own building and having uninterrupted take linkage with companies in the RPS sector and good record of performance are eligible for the assistance the board expects that the usage of IT –enabled services among the rubber plating community will empower the growers with latest information in the field of cultivation world over and market situation internationally. (6) Rubber board chairman “Financial aid to form producer’s societies” about The rubber board has announced financial assistance to support formation of Rubber Producer’s Societies (RPSS) and Self Helps Groups(SHGs) This scheme has provision to support at the socio-economic development of the poor farmers and their families. It is intended to promote group approach for the effective modernization and improvement of the NR sector, as individual approach is not practical owing to the large number of such holding. The rate of assistance is Rs 6,000 for the formation of new RPSs and Rs. 3000 for SHGs. (7) Cyril Kanmony and Gnana Elplinston (2010) analysed the impact of climate change on important crops in Kanyakumari district. They have reported that the climate 37

ISBN: - 978-93-88936-09-5 conditions particularly the rainfall pattern of any country affect the area under cultivation, production and productivity of important crops like paddy, wheat, sugarcane and rubber. So the conversion of agricultural land into non-agricultural land has been going on in a great speed. They have suggested the district authorities to take suitable action not only to mitigate adverse impact of climate change but also the agricultural land Converted into non-agricultural land. (8) Saikundan, Saivamshimedak, in their article “What Makes rubber Stretchable?” Try to establish the following facts: Rubber is a natural polymer and possesses elastic properties. It is also termed elastomeric and has a variety of uses. It is manufactured from rubber latex which is a colloidal dispersion of rubber in water. This latex is obtained from the bark of the rubber tree. (9) Dharma raj. E, (1992)8, in his study entitle, “Natural Rubber Production in India”, states that adoption of high yielding varieties of rubber in the small holdings sector was unnoticed for a long time due to its insignificant contribution towards production and productivity of rubber. However, since 1960 many small rubber growers began to plant high yielding varieties of rubber seedlings. (10) The Hindu Business Line dated on October 12, 2010 stated that, Association of NR producing countries (ANRPC) by the chairman of rubber board. ANRPC is an intergovernmental organisation established in 1970, and has 11 member countries Cambodia, China, India, Indonesia, Malaysia, Philippines, Singapore, Sri Lanka, Thailand And Vietnam. The member countries account for around 92 per cent of NR production in the world. 2.1 (b) REVIEW RELATING TO NATURAL RUBBER TAPPERS (1) Preetha. M, (2003), A Socio Economic Study of the Rubber Plantation Tappers in Cherupalors Village”, shows that, the employment in tapping is seasonal in nature. During the peak season the tappers are able to work an average of 17 days a month. During off-season tapping is not possible and therefore, the tappers would not get tapping. (2) It is fruitful to analyse the socio economic profile of rubber tappers in the light of the concept of ‘Livelihood Diversification’. Frank Ellis defined this concept as the process by which rural families construct diverse portfolio of activities and social support capabilities in their struggle for survival and in order to improve their standard of living. The evolution of technology in the rubber economy of Kerala and, its development in various stages of rubber cultivation and production are widely 38 ISBN: - 978-93-88936-09-5 discussed and analysed in The Hand Book of Natural Rubber, published by the Rubber Research Institute in Kottayam. One important problem now faced by rubber economy of Kerala and elsewhere is the non-availability of skilled labourers. (3) The Hindu Business Line Dated on 16 October 2010 stated that, According to Tileke Ratne and Nugawela the use of rain guard enabled not only to maximize natural rubber production but also to reduce seasonal unemployment of rubber tappers. Lack of skilled tappers is considered as the emerging problem in smallholdings. (4) The Hindu Business Line Dated on 15 October 2010 stated that, According to Ng Kok Tee, labour shortage is one of the major problems behind the decline in natural rubber production in Malaysia. There is a move of production factors from agriculture sectors to other sectors and the shortage of labour has been accelerating this process. (5) Pushpa Rajah in one of his articles points out that Malaysia has given more importance to the innovations in labour saving techniques of rubber cultivation. He states that future of Malaysian rubber industry will brighten up only by making rubber cultivation a less labour-intensive system particularly in tapping. The study predicts that in the near future, the approach to rubber as a monocarp solely for latex will slowly phase out. Then rubber will be planted for the timber as a primary product and latex will be an important by- product. (6) Parthiban Gopal(2004) in his study “The Rubber Tappers’ Monthly Wage Issue and the impact of the Collective Agreement of 2003”, has made the following observations the legitimate quest for a guaranteed stable level of income for rubber tappers in Malaysia may have been delayed unduly because the solution was thought to lie in a monthly wage scheme. But on theoretical and practical grounds, a monthly wage scheme was inappropriate in an estate setting. This realization came only in 2003 when a guaranteed threshold income of Rs 35,000 a month was obtained within the context of the existing framework of payment. The threshold income, although not large, exceeds the poverty line income (per capita). However, it is better viewed as a guarantee of stable income in usual situations when weather, land yields and agronomy conspire to deny the tapper the usual level of earnings. To the extent that these situations will not arise frequently, and most households have at least two working members, the debate whether the threshold is sufficiently high or not may have less practical significance. Nonetheless, an important concession has been won from the employers and the Union can seek to improve on this in future negotiations. 39

ISBN: - 978-93-88936-09-5 Finally, reported average earnings under the 2003 Agreement are much higher than under the agreement before it both in nominal and real terms, suggesting some improvement in overall earnings were indeed achieved. (7) Benin City, Nigeria”, the study was conducted to analyse the technical inefficiency of rubber tapping in Rubber Research Institute of Nigeria Benin City, Edo State. Time series data of 129 tappers were analysed using stochastic frontier analysis. The tappers were sampled using simple random sampling technique. The result of the stochastic frontier production function revealed that the variance of parameters (gamma and sigma squared) of the frontier production function were both significant at p<0.01. There were substantial variations in estimated efficiencies ranging from 0.38 for the least practiced tapper and 0.99 for the best tapper with a mean technical efficiency of 0.72. However, the inefficiency model revealed that education, training and gender were found to have significant effect on tappers efficiency at one percent probability level. It was however recommended that addressing the tappers specific factors would reduce inefficiency in rubber tapping. (8) Rodrigo (2007) in his research paper “Adoption of different tapping systems in the rubber industry of Sri Lanka with special reference to low frequency tapping”, presents the following argument Tapping is the most costly activity in natural rubber production and the shortage of tappers is a serious problem in rubber plantations in Sri Lanka. Low Frequency Tapping (LFT) systems are considered to be a solution to these issues by reducing the number of tappers required and increasing tappers' income. With no information available on the adoption of different tapping systems, the present study was aimed to assess the existing tapping systems and associated effects on the productivity and other related issues in both plantation and smallholder sectors in Sri Lanka. Also, focus was given to identify the factors hindering for the adoption of LFT. Information was initially gathered using questionnaire based surveys and then verified through participatory workshops. The study revealed that the poor productivity in the plantation sector was highly associated with the shortage of skilled tappers. Productivity of the smallholdings tapped with family labour was less than that of smallholdings tapped with hired labour and this had been associated with the quality of tapping. In general, incorrect agro management practices have resulted in poor productivity. With no proper knowledge, the adoption of the LFT was rather poor in the plantation sector and zero in the smallholder sector. In view of addressing the issues related to the tapper shortage, an effective programme was proposed with the application of LFT. It 40 ISBN: - 978-93-88936-09-5 comprised with improved crop management practices and proper incentive schemes that transmit some benefits of LFT to tapper. Tapper training programmes and large scale demonstrations with model cluster systems were identified as primary needs for the effective introduction of LFT. (9) David S. Salisbury a, Marianne Schmink, (2008) in his study titled, “Cows versus rubber: Changing livelihoods among Amazonian extractivities”, the livelihood strategies of former rubber tappers in the Amazon region are rapidly shifting from extraction of non-timber forest products to mixed systems based on agriculture and small scale cattle ranching. Using a combination of participatory methods and Geographical Information Systems, a case study in western Acre, Brazil explores how rubber tapper livelihood strategies may be changing, and the implications of these changes for land use and forest cover. Field (cattle pasture and agriculture) expansion and the decline of forest extractives present challenges to many regional conservation and development projects such as sustainable settlement projects and extractive reserves seeking to develop forest-based livelihood alternatives to limit deforestation. Sustainability goals require researchers and policy makers to address the still experimental status of these forest-based organizational units, the heterogeneity and dynamism of extractives livelihoods, and the necessary importance of small-scale cattle ranching for insurance and income generation among many former and current extraction. (10) V.Verma (1993), in this book “Tapping of Rubber” to prove that the latex is obtained from the numerous latex as on the bark, which is arranged in connective rings alternating which rings of phloem. The inner part contains more verses than the outer part. The bark is cut on such a manner that the delicate growth layer of cambium is not damaged. Since the latex verses run spirally to right at an angle of 300 of the vertical pore, tapping is made from the upper left to the lower right at a 300inclination to obtain maximum yield. Tapping is begun in 6 years old trees. 2.1 (c) REVIEWS RELATING TO MARKETING OF THE NATURAL RUBBER (1) Antony Michel. K. (1993), in his study on “The Rubber Plantation Workers in Kanyakumari District-An Empirical Study”, opines that, the rubber plantation workers faced with two important problems. They are excessive increased in their in debentures and addition to creditors. These two problems have considerably retrained the growth of the material prosperity of the rubber plantation workers in Kanyakumari District. 41

ISBN: - 978-93-88936-09-5 (2) Manju Stephenson is her study states that price determination depends upon demand and supply of the rubber product. The dry rubber sheet only provides the high incomes when compared to other categories of rubber sheet like wet sheet and raw materials in milk rubber. (3) Rubber board Chairman Sajeen Peter (2006) says that the rubber export exceeded 28617 tonnes during the first quarter of the current fiscal as against 5056 tonnes of exported during the same period of last year, recording 5.7 per cent increase. (4) Central office, Kottayam, Chairman (2007) opines that exports are expected to increase as long as the international prices rule higher than the domestic price and as such exports are necessary also. The stock position on June 30 last year was 89696 tonnes and this year it has come down to 64000 tones, he added. During the first quarter of 2006-07 the import was 20551 tonnes whereas it was 28132 tones, last year. During the last fiscal, production reached 8002625 tonnes attaining a growth rate of 7.1 percent while consumption registered a growth rate of 6.1 percent touching 801110 tones. According to board’s expectation rubber production during the current fiscal year would touch 831000 tones and consumption 841000 tones. Chairman also pointed out that the import expected was 45000 tonnes and export 50000 tonnes. The natural rubber consumption in all the countries except Malaysia increased and the world consumption was 87.8 lakh tones last year, registering a growth rate of 5.2 percent. (5) The Hindu Business Line dated on 28, October 2010 stated that According to traders, sheet rubber improved to Rs. 190 from Rs. 189.50 a Kg on fresh buying and short covering. An already tight situation in global supply of NR is expected to worsen further in the fourth quarter of the year. (6) The Hindu Business Line dated on September 15, 2010. Dr.Aravindan said that, “Mixed trend in rubber”. Traders stayed back on sheet rubber possibly sense its futures on NMCE shed the gains partially followed by selling at higher levels. The reports from the international market were also depressing. Sheet rubber market flat at Rs.169 per Kg in main market for RSSIV decline to Rs 170.19(172.46) (7) Rubber drops on global gulf worries: Rubber dropped passing an earlier advance am oil concern that demand in china may slow while a global surplus of the commodity used in tyres widens. The contract of commodity delivery on June in the Tokyo. Exchange drooped to close at 2.773 a kg ($2607) a tonnes rerating from a high of 277.8 futures have dropped 10 per cent this year. 42 ISBN: - 978-93-88936-09-5 (8) The Hindu Business line dated on Dec 25- 2013 stated that the physical rubber price were mixed on Tuesday the market war in a holding mood On the every Christmas major counters were steady even in the absence of quantity sellers but ungraded rubber dropped marginally amidst scattered transaction ISNR 20 and latex improved furthered better demand while overall volumes were dull shell rubber finished unchanged at RS 160 a kg at kottayam and cochin accounting to traders and the rubber board. (9) The Annual Rubber Conference 2010 hosted by the Government of India held in Cochin on October 6.2010. The NR markets in the last two year have been governed by prediction and uncertainties in supply. Some visualized an oversupply of NR after 2012 on account of extensive planting area since 2005. (10) The Hindu Business line dated on Dec 19 - 2013 stated that the Spot rubber price were almost steady on Wednesday the market lost its direction despite a firm closing in domestic futures as there was no follow up buying by major consuming industries RSS-4 suffered marginally during late trading hours, while overall volumes were extremely atoll in most counters. 2.2 (d) REVIEWRELATING TO RUBBER BASED INDUSTRY (1) Mooharjee.K.N, (1968), in his study opines that, the need of the hour in the rubber industry is expansion and diversification. The rubber industry in India has been taking very great efforts to expand and diversify the production activity. Thus, today the rubber industries produces hydraulic breaks and air breaks with comprise highly specialized automatic applications. The techno creation of India to innovate highly sophisticated technologies which can ultimately facilitate the growth, expansion and diversification. (2) Chand Nair. (1969), is of the view that, the price of natural rubber has been under control. Since 1942 with short breaks both maximum and minimum prices for various grades of rubber have been fixed by the government only. He holds that these prices are comparatively higher than those in other natural producing countries. The rubber industry pays Rs. 300 per ton or excise duty for every tone of rubber purchased to the Rubber Board which is statutory body looking after the interest of the rubber plantation industry. This amount is collected from the industry for the betterment of plantation (3) Patel N.K, (1972), in his study finds that, the rubber industry is basically associated with chemicals, plastics, paints, lace and glass. However it cannot be denied that this is a strategic industry both in times of peace and war. He adds that the industry has 43

ISBN: - 978-93-88936-09-5 started developing steadily in an organized manner during the plan periods. Consequently the industry plays a vital role in the economy of the country which caters to the country entire needs in almost all the fields including defence, communication, agriculture and a number of other industries including textiles, pharmaceuticals Patel feels that the rubber industry has plenty of scope for department in the years to come as it does not have any serious bottle neck with regard to the availability of raw materials. (4) Lalithambika J. has made the following observation on the consumption of rubber. The demand for rubber depends on the growth in production of rubber products for domestic use as well as for export. In India, 93 per cent of the rubber is used for manufacturing rubber products for domestic consumption only seven per cent is processed into value added for export production. About 65 percent of the rubber goes for tyre and tyre products. In view of the massive expansion plans of the tuber goods manufacturing sector and the increasing exports, it will be difficult to reach selfsufficiency in natural rubber. The growth in demand is likely to be faster than what the plantation industry can supply. Exporting raw rubber can be an objective as the price of natural rubber in the international market is lower than that in India. (5) The Hindu Business Line in 2010 in its article, “Low priority for rubber in carbon trade irks planter” has given the data: Domestic market sought to encourage a forestation. The plantation industries, especially rubber growers, are feeling aggrieved. Plantation crop provide an array of ecosystem services such as protecting the top sail from erosion improving the water cycle and carbon request ration. But it does not enjoy the benefits of this since annual return is not among preferred in 15 sectors that are eligible for carbon trading. (6) Tharian George K. Toms Joseph Joby Joseph in their study explained that at the three trades control measures were introduced on natural rubber by the government of India. The new control measures are declaration of statutory minimum prices for RSS.4 and % effective from September 2, 2001 restriction of natural rubber imports only through the designated ports of Kolkata and Vishakhapatnam effective from December 10, 2001. Mandatory quality standards for both domestically processed and imported natural rubber in conformity with the standard specified by the Bureau of Indian Standards effective from December 12, 2001. Accordingly two perceived objective of new measures are to stabiles `domestic natural rubber prices at desired level and restrict import so as to prevent further deterioration in prices. 44 ISBN: - 978-93-88936-09-5 (7) Sangltani (1973), in the words of, the Indian Rubber Industry manufacturers almost every conceivable rubber product from tiny balloons to giant tractor and aero tyres needed for internal consumption as well as for export. The industry is already catering to the requirements of defence like pharmaceuticals textiles and furniture as well as agricultural options. The industry has to depend still on foreign countries for certain vital and sophisticated rubber products. He feels that a major break in this areas should be attained it’s the rubber industry is given a good exposure to the use of certain modern technology. (8) Muniyandi, B.; Bankaranarayanan, S.; and Chellan, K. (1997) in there article “Marketing of natural rubber. A case study in Kalkulam Taluk of Kanyakumari District” the study reveals that the natural rubber plays and indispensable rule in manufacturing a variety of product. The world today uses as many as 5,000 different rubber products. India occupies a prominent position in rubber goods manufacturing and produce over 35,000 individual items. At present India occupies the fourth position in the production of natural rubber at global level, production was increased to 3, 93, 490 tons in 1992-1993, Which was about 25 times more compared to 15,830 tons in 1950-1951.The rubber industry is a labour absorption and export earning in industry. The number of labours engaged in rubber touched 3.05 lakhs, about Rs.671.85 crores was earned as foreign exchange through export of rubber goods in 1993. (9) The Hindu business line was stated that “Take Advantage of the price differentiating spot, forward markets Kochi” as future of natural rubber continues to grow, arrivals at warehouse of commodity exchange have increased enabling formers and arrivals of rubber at the exchange were house that increased 89 percent year 2001. 10)Ahmad Mahdzan Ayob and Antony Prato in their study on “An Economic Analysis at the United States point import demand and prices for natural rubber”, department at agriculture economic publication, at Florida Gainesville august 1971, The development of rubber economy of Kerala is an important step towards the advancement of agriculture sector in the state. There have been a number of studies about the spectacular development of small holding rubber sector and its dominant role in the total production of natural rubber. But these studies deal only with productivity, technology adoption, credit facilities and different schemes of governmental agencies to encourage production. 45

ISBN: - 978-93-88936-09-5 It is unfortunate that there has not been any specialized and comprehensive study on the problems of the unorganized tappers in the smallholdings. However, there are some studies about the agricultural labourers and plantation workers in the state. These studies are, to some extent, applicable to tappers as well. 2.2 CONCLUSION FROM THE LITERATURE REVIEW • There is a need for an efficient marketing organization in the small holding sectors and the support for receiving the real price of rubber sheet. • The tapping is a skilled work which needs proper training and guidance. A good tapping enhances the productivity. • As cost and income flows occur in different years, any financial analysis should be based on the present values of the cash flow rather than the actual values. • There are different rubber tapping techniques for increasing the rubber yield and productivity. The study found that the main factor leading to declining profit per hector of rubber plantation were labour problems and increasing wage rates. • The basic components of management in rubber plantation which are applicable to both traditional and non-traditional area are material and processing management, marketing management, financial management, labour management and farm management involves three elements viz, the scarcity of resources, their alternative uses and the objective of profit maximization. REFERENCES 1. Costs and Prices of Raw Rubber Tariff Inquiry Commission (1967), April 15, 1967, p.668. Rubber Small Holdings Economic Enquiry Committee, Government of India. 2. Bai Leela Baby (1990), “Location and Organization of Small Scale Enterprises in Kanyakumari District” Volume 35, Issue No. 15-18, pp. 23-25, Bangalore. 3. Gangadharan P.K.(1991) “Optimum Utilisation of NR in Rubber Industry in Kerala” p.67 4. Economic Survey 2009-10. 5. The Hindu Business Line Dated on March 2011. 6. Cyril Kanmony and Gnana Elplinston (2010) the impact of climate change on important crops in Kanyakumari district. 7. Sajeen Peter “Financial Aid to form producer’s society”. RBB-Kerela 8. Saikumdan, saivamshimedak, (1980) “what makes rubber stretchable?” RBI , Regional Office, kerela. 9. Dharmaraj (1992), “Natural Rubber Production in India”, Kisan World, Vol. 19, No. 12, pp. 30-31, Delhi. 10. The Hindu Business Line Dated on October 2010. 46 ISBN: - 978-93-88936-09-5 11. Preetha. M. (2003), “A Socio Economic Study of the Rubber Plantation Tappers in Cherupalors Village”, Unpublished Project report to Manonmaniam Sundaranar University, Department of Economics, Scott Christian College, and Nagercoil. 12. Frank Ellis ‘Livelihood Diversification of rubber tappers’ unpublished project report to Madurai Kamaraj University. 13. The Hindu Business Line Dated on 16 October 2010. 14. The Hindu Business Line Dated on 15 October 2010. 15. Pushpa Rajah a (2007) Rubber Board Bulletein, Vol.28 no.3. 16. Parthiban Gopal “The Rubber Tappers’ Monthly Wage Issue and the impact of the Collective Agreement Of 2003”, Kajian Malaysia, Jld. XXII, No. 2, December 2004, pp.63-79. 17. Giroh. D.Y. and E.F. Adebayo “Analysis of the Technical Inefficiency of Rubber Tapping in Rubber Research Institute of Nigeria, Benin City, Nigeria”, Journal of Human Ecology, Vol.27, Issue.3,2009, pp.171-174. 18. Rodrigo, “Adoption of different Tapping Systems in the Rubber Industry of Sri Lanka with special reference to Low Frequency Tapping”, Journal of the Rubber Research Institute of Sri Lanka, 2007,pp.121. 19. David S. Salisbury a, Marianne Schmink, (2008), “Cows versus rubber: Changing livelihoods among Amazonian extractivists”, in Amazon. 20. V. Verma, tapping of rubber The Hindu Survey of Indian Agriculture, Annual Hindu Publication, Chennai, 2004, pp.163. 21. Antony Michel. K. (1993), “The Rubber Plantation Workers in Kanyakumari District-An Economic Study”, Unpublished Dissertation to Manonmaniam Sundaranar University, Department of Economics, Scott Christian College, Nagercoil. 22. Manju Stephenson “ Production and Marketing of NR in Kanyakumari District”, Unpublished Dissertation to Manonmaniam Sundaranar University, 23. 1 Sajen Peter, “Rubber export registered a record 28617 tonnes”, Rubber Board, June 30, 2006. 24. Central Office, Kottayam, Chairman, “Rubber Export registered a record 28617 tonnes”, Rubber Board, June 30, 2006. 25. The Hindu Business Line Dated on 28, October 2010. 26. The Hindu Business Line Dated on 15 September 2010. 27. The Hindu Business Line Dated on 10 February 2012. 28. The Hindu Business Line Dated on 25 December 2013. 29. The Annual Rubber Conference, 2010 hosted by the government of india held in Kochin on 6th October 2010. 30. The Hindu Business Line Dated on 19 December 2013. 31. The Annual Report of Kanyakumari District 2000-2001. 32. Narayanan Nair P.N., “The Rubber and its Cultivation”, the Rubber Board, Kottayam. 2001, p.80 33. Mooharjee,K. N., “Heavy Demand calls for Substantial Expansion”, Commerce, July 6, 1968, p. 35. 34. Chand Nair (1969) “Price Control Mechanism in Rubber Industry,” Commerce, Annual Number 1968, p.221. 35. Patel, N.N.K., “Raw Materials for the Rubber Industry”, Commerce, June 17, 1972, p.159. 47

ISBN: - 978-93-88936-09-5 36. Lalithambika, J.”Rubber Challenges from rising demand”, The Hindu Survey of Indian Agriculture, Annual Hindu Publication, Chennai, 1994, pp.83-84. 37. Tharian George K., Toms Joseph Joby Joseph, “Natural Rubber in Post – QRS Regime”, Economic and political weekly, Vol.XXXVII, No, 32, August 10, 2002, p.319. 38. Muniyandi, B.; Bankaranarayanan, S.; and Chellan, K. (1997) “Marketing of natural rubber. A case study in Kalkulam Taluk of Kanyakumari District” 39. The Hindu Business Line Dated on January 23 2012. 40. Ahmad Mahdzan Ayob and Anthony Prato, (1971) “a economic analysis of the united states important demand and price of nature rubber”, Rubber Asia vol. XXXV p.107. 48 ISBN: - 978-93-88936-09-5 2.3 PROFILE OF THE STUDY AREA 2.3 (A) TAMILNADU Tamil Nadu is situated on the south eastern side of the Indian peninsula It lies between 8.5° South 13.35° north 76.15° east and 80.20° west. It is bounded by the Bay of Bengal and the Indian Ocean, Arabian Sea in the south, in the west by the state of Kerala and Karnataka and Andhra Pradesh. Tamil Nadu has an area of 1, 30,058 square kilometres and its capital is Chennai. Tamil Nadu is divided in to 32 districts. 2.3 (B) PROFILE OF KANYAKUMARI DISTRICT 2.3 (B) 1.. INTRODUCTION OF KANYAKUMARI DISTRICT: Kanyakumari district came into existence in the year 1950 as a result of the reorganization of states on the basis of vernacular and was added to the map of the Tamil Nadu. It was known as the granary of Travancore due to its vast stretches of paddy fields, rich forests and abundant mineral sands. This district has a pleasant climate and has the advantage of both the South-West as well as the North-East monsoons. It has many charming tourist spots the best being the capeComorin, the meeting place of the Bay of Bengal, the Indian Ocean and the Arabian Sea. The meeting point of the seas is the place where the “Continent ends in a swan-song of broken rocks and mingling oceans”. Kanyakumari district has been named after the Goddess Amman, who is the favourite deity of large number of people of this district. The legend is that the Goddess Parvathi has taken incarnation as Devi Kanya and did penance on one of the rocks of this land’s end to obtain the hand of Lord Siva. 2.3 (B) 2. Ancient History Kanyakumari district consists of two parts locally known as Nanjil Nadu and Idai Nadu. The names of the villages of the district such as Azhagiapaandipuram,, Bhootha Pandy, Cholapuram and Kulasekaram reveal that these places were governed by several rulers at different periods of time. It is learnt that Nanjilnadu was under the rule of the Padiyas till the early 10th century and then under the Cheras. The Idai Nadu which includes Kalkulam and Vilavancode taluks, was under the rule of the Cheras. When the power of the Cholas declined due to the rise of Hoysalas and Western Chalukyas, the Vennad (Travancore) Chief-taint took advantages of the situation and gradually established their hold on considerable areas in Nanjilnadu. Veera Kerela Varma was one such chief-taint who styled himself as Nanjil Kuravan. 49

ISBN: - 978-93-88936-09-5 2.3 (B) 3.General Information of Kanyakumari District Kanyakumari district is situated in the Southern most end of Indian Sub-Continent. The District has a large number of historic monuments and temples. A series of kingdom are known to have ruled Kanyakumari and most important being the Chera, Chola and Pandya. On the recommendation of the commission Agastheeswaram, Thovalai, Kalkulam, Vilavancode and Shenkottai taluks were given to Tamil Nadu among which the first four were grouped to form a new Kanyakumari district in Tamil Nadu. It is predominantly an agricultural region. The district has a total area of 1672 square kilometre. 2.3 (B) 4.SOIL CONDITION In Kanyakumari district the soil is acid in nature. The contents including nitrogen, phosphoric acid, potash and limes are found to be 0.08 percent, 0.02 percent, 0.09 percent and 0.17 percent respectively. 2.3 (B) 5.AGRICULTURE Agriculture is the main occupation of this district. This district procures paddy, tapioca, and oil seeds such as Groundnut and Coconut besides, commercial crops like cashew, rubber, fruits and spices. The important feature of this district is the production of seasonal Mangoes. 2.3 (B) 6.SPICES AND OTHER PLANTATION Spices like cardamom, clove, and pepper are also cultivated in kanyakumari district. No other region in Kanayakumari district is suitable for the production of spices. Paddy is the main crop of this district. It is grown in two seasons. First crop is sown in the months of April – June (Kannipoo) and second crop is raised in the months of September (kumba Poo) Tapico is raised as a subsidiary food crop in Kanyakumari District. The highest planting season in April – May. Coconut is the main cash crop which occupies the second position in the gross cropped area and major portion of the non – good crop area of the Kanyakumari district. Banana is cultivated mainly during March- May and September- October. Groundnut is raised during March - April and October – November as rainfall crop to a small extent. 2.3 (B) 7.FORESTS Due to rich soil and favourable rainfall the taluk is endowed with a very valuable tropical evergreen forest. Elevation of forest from the sea level has been estimated at 1850 meters under regeneration programme acacia (babul) and eucalyptus species are planted in the forests. 50 ISBN: - 978-93-88936-09-5 The important species of trees found in these forests include teak, rose wood, wildjack, Manjalladambu, Ventlok, Vengali, Pilljamaradhy and kurumarudhy. Minor forest products are available like bamboo, Cane, reed, Lemon, Lenon, gross and medicinal plants. 2.3 (B) 8.TEMPERATURE The temperature which is normally between summer 27° C (81° F) and winter 16° C (61°F) offered good weather condition and quite favourable for the cultivators 2.4 THE RUBBER BOARD Kanyakumari District of Tamil Nadu provides the best suitable climatic conditions for planting Rubber. The rainfall pattern, temperature range, wind speed etc prevailing this district is already suitable for planting Rubber. The Indian rubber board was constituted under the Rubber (Production and Marketing) Act 1947. This act was passed on the recommendation of an adhoc committee appointed by the Government of India in 1945 and it came into force on 19 April 1947. The Rubber production and Marketing (Amendment) Act of 1954 made certain changes and renamed as “The Rubber Board”. The Rubber Board helps the small growers get subsidy from the central government. The head office of the rubber board is in Kottayam. These are so many branches related with Kottayam main office; the branches are in Nagercoil, Marthandam, Manalodai in Kulasekharam area. The rubber board was started about 50 years back. These offices were started for the benefit of small growers this will enhance the production and marketing of rubber. (a) Functions of Rubber Board The functions of the board as defined under the Act are:  To promote the development of the rubber industry.  Undertaking, assisting or encouraging scientific, technological or economic research.  Training students in the improved methods of planting, cultivation, manuring and spraying.  The supply of technical advice to rubber growers.  Improving the marketing of rubber.  The collection of statistics from owners of estates, dealers and manufacturers.  Securing better working conditions and the provision and improvement of amenities and incentives to workers. (b) Duty of the board 51

ISBN: - 978-93-88936-09-5  To advise the central Government. On all matters relating to the development of the rubber industry including the import and export of rubber.  To advise the Central Government with regard to participation in any international conference.  To submit to the Central Government. and other such authorities as may be prescribed half yearly reports on its activities and the working of this act and  To prepare and furnish such other reports relating to the rubber industry as may be required by the central government from time to time. (c) Constitution The rubber board functions under the Ministry of Commerce and Industry of the government of India. The board has a chairman appointed by the central government. He is the principal executive officer responsible for the proper functioning of the board and implementation of its decisions. There are 25 other members of the board consisting of: Two members to represent the state of Tamil Nadu. One of them shall be a person representing rubber producing interest.  Eight members to represent the state of Kerala. Six of them shall be representing the rubber producing interest, three of such being persons representing the small growers.  Ten members to be nominated by the central government two of whom shall represent the manufacturers and four labour communities.  Three members of parliament two of whom shall be elected by the house of the people and one by the council of states.  The Rubber Production Commissioner of the Rubber Board  The executive director. (d) Rubber Production Department The department headed by the Rubber Production Commissioner is responsible for planning, formulation and implementation of schemes for improvement and expansion of rubber cultivation and production. The following activities are undertaken by Rubber Production Department  Rubber plantation department scheme  Production and distribution of improved planting materials  Advisory and extension services to growers  Demonstration of scientific planting and production  Supplies of equipment and materials requiring popularization. 52 ISBN: - 978-93-88936-09-5  Promotion of Self Help Groups among small growers.  Training of tappers. (e) Rubber processing and market development division  Implementing schemes for financial assistance to Rubber Processing Schemes, Cooperative Societies and companies jointly promoted by Rubber Processing Schemes and the board  Organising training programmes and extension activities in the area of rubber processing for growers, Rubber Processing Schemes processing companies and cooperatives societies for quality up gradation.  Providing training in grading and grading support to others. (f) Market Promotion Department The Market Promotion Department functions under the direct control of the chairman. (g) Market Intelligence Cell a) Collection, compilation and dissemination of natural rubber prices. This includes the daily, weekly, bi-weekly, monthly and yearly prices of various grades of natural rubber in the domestic as well as in the international market. The price data are disseminated through print and visual media. b) Providing sales and marketing support to companies promoted/assisted by the rubber board. c) Conducting market surveys and market analysis. d) Publishing the directory of Rubber Goods Manufactures in India 2.5 ASSISTANCE FROM THE RUBBER BOARD FOR QUALITY IMPROVEMENT The Rubber Board has introduced a financial assistance scheme for giving transportation subsidy to the Rubber Producer’s Societies (RPSs) engaged in collection and transportation of latex from plantations to the collection/processing centres’ with a view to reducing the border by way of transportation. The assistance will be at the rate of 25 paisa per kilogram of dry rubber collected as latex. Only those RPSs which are engaged in latex collection or sheet processing and trading through the Rubber Board’s companies or marketing co-operatives are eligible for the assistance. By the scheme, the Board aims at increasing the trading share of the RPSs, quality improvement of their produce, price competitiveness and there by wider acceptance of Indian Natural Rubber in the world market. 2.6 ARASU RUBBER CORPORATION LIMITED, Nagercoil 53

ISBN: - 978-93-88936-09-5 (a) Field rubber production The degree of 4279.78 ha, of forest land is being managed under rubber cultivation on lease from the forest department. Tapping was done in 2037 blocks. The corporation has achieved a production of 2030 tonnes of rubber against the target of 2050 tonnes during the year. The division wise NR production from 2002-03 and 2004-05 by the Arasu Rubber Corporation, Nagercoil. There are five divisions namely Mylar, Kodayar, Chithar, Manalodai, and Keeriparai. Rubber production from 2002-03 to 2004-05, based on divisionwise production have made. 2004-05, 4785 ha of forest land was taken over on lease basis from the government of Tamil Nadu. Out of this, an extent of 505.92 ha.has been handed over to the forest department during the year 1998-99 as per G. O Rt.No. 367 Environment and Forest (FR VII) department dated on 03-09-1997. The following table explain the divisionwise production of NR by Arasu Rubber Corporation, Nagercoil. Table 2.1 Serial Name of the division 1. Keeriparai 2. Manalodai 3. Chithar 4. Mylar 5. Kodayar Field Rubber Production No of Total area in ha. 953.30 968.90 859.58 612.30 885.70 Blocks 329 518 438 329 423 Collection of rubber in M.T 316 461 432 339 465 . Source: 26th Annual Report of ARCL The above table depicts that the division wise production of natural rubber by ARCL, Nagercoil. There are five divisions such as Mylar, Kodayar, Chithar, Manalodai, and Keeriparai. Maximum rubber production is made by Manalodai division. An extent of 4279.78 ha of forest land is being managed under tuber cultivation on lease from forest department. During the year 2012-13 tapping was done in 2203 blocks. The corporation has achieved production of 1682 tonnes of rubber against the target of 2000 tonnes during the year under report. During the 2012-13 year, the production performance of processed rubber was as detailed below. Table 2.2 Rubber Production Grade Grade EBC Grade Keeriparai rubber factory 514 54 Mylar rubber factory __ Total (in tonnes) 514 ISBN: - 978-93-88936-09-5 Cenex Skim crepe Others Total 562 61 4 1,141 763 80 11 855 1.325 142 16 1998 Source: 30th Annual Report of ARCL The above table shows that the production of different varieties or categories of rubber sheets for 2012-13 by the ARCL, Nagercoil. The EBC grade was in 514, Cenex was in 562, skim creepe was in 61, and other was in 4 tonnes of rubber sheets were produce in Keeriparai blocks. Cenex was in 763, skim creepe was in 80, and other was in 11 tonnes of rubber sheets were produce in Mylar blocks. 2.7 SALES IN RUBBER The ARCL sole the EBC, Cenex, Skim Crepe and Others Grade quality of rubber sheets are sold in the market. In 178 tonnes of grade RSS I high quality of rubber sheets was sold only in the market in 2004-2005. During the year 2005-06, 2638 tonnes of rubber was sold and a revenue of Rs. 1872.83 lakhs, in 2004-05, 2068 tonnes of rubber was sold and a revenue of Rs. 1252.43 lakhs, 2510 tonnes of rubber was sold revenue 1450.35 lakhs in 200304, and Perunchani rubber factory , there was no production made during the study period. During the year 2007-08, 2056 tonnes of rubber was sold and revenue of Rs. 1940.77 lakhs has been realized. During the year 2008-09, 1723 tonnes of rubber was sold and revenue of Rs. 1779.31 lakhs has been realised.During the year 2012-13 the production performance of processed rubber was as detailed below. Table 2.3 Factory Production Grade EBC Grade Cenex Skim crepe Others Total Keeriparai rubber Mylar rubber 388 460 50 17 __ 662 75 40 916 741 1122 125 21 1657 Source: Annual Report of ARCL The above table shows that the production of different varieties or categories of rubber sheets by the ARCL, Nagercoil. The EBC grade was in 388, Cenex was in 460, skil creepe was in 50, and other was in 17 tonnes of rubber sheets were produce in Keeriparai blocks. 55 Total (in tonnes) 388

ISBN: - 978-93-88936-09-5 Cenex was in 662, skil creepe was in 75, and other was in 40 tonnes of rubber sheets were produce in Mylar blocks. 2.8. RUBBER INDUSTRY IN INDIA The Indian rubber industry is posed to grow with increased potential in the days to come, both in terms of strength and dominance. The robust domestic demand and the increasing global prospects for natural rubber have made it inevitable for the industry to retain its domain as a vital component. India is the fourth largest producer and second largest consumer of NR in the world. Increasing prices and improved has provided a resurgent incentive for the rubber growers to produce more which has resulted in increased production India is considered as one among the fastest growing economic globally. There are about 4600 registered units comprising of 30 large scale, 300 medium scale and around 4400 small scale and tiny units which form part of the colossus of the natural rubber industry in India. Indian rubber industry is unique in the sense it is a major producer and consumer of natural rubber. Though rubber products manufacture started in the country in the year 1920, rapid growth in the last four decades has transformed the rubber products manufacturing industry one of the important sectors of the Indian economy. Considering the large population and the large manufacturing base particularly in the automobile industry and the availability of competitive labour, the country offers great opportunities for rubber product manufacture. With further investments in Research and development infrastructure, the country is poised to become a leader in rubber products manufacture in the years ahead. The world rubber production was considered to be very unstable during the last few years. Comparatively, India production of rubber is consistent at the rate of 6 percent p.a. The rubber industry in India has been growing in strength and importance. This is the result of India’s burgeoning role in the global economy. India is also one of the fastest growing economies globally. These factors along with high growth of automobile production and the presence of large and medium industries have led to the growth of rubber industry in India. 2.8(A) STRUCTURE OF INDUSTRY The two broad groups of the Indian rubber industry are the tyre and the non-tyre sectors, the former promoted mostly by large industrial houses and multinational companies and the latter comprising mostly by small and medium scale units. Many of the units in the small scale sector are tiny units consuming less than 10 tonnes of rubber per annum. It is pertinent to point out that the total number of units decreased from 5066 in 2001-02 to 4327 units in 2008-09. This is mostly on account of the closure of many SSI / tiny units which could not survive the present highly competitive environment and the high cost of raw 56 ISBN: - 978-93-88936-09-5 materials. 2.8 (B) CHARACTERISTICS OF THE INDIAN RUBBER INDUSTRY  The following are the special characteristics of the rubber industry in India which make India little different from many of the other natural rubber producing countries.  India is a major producer and consumer of natural rubber. The rubber products manufacturing industry in India has been mostly inward oriented, catering to the domestic market.  In recent years India has entered the global market, exporting both raw natural rubber as well as rubber products  With its large population base, India is emerging as a large market for rubber products and with the opening up of the economy, and import of rubber products has also been increasing in recent years.  The rubber product mix in India is based mostly on dry forms of rubber, dictated by the requirements of the domestic market. 2.8(C) MANUFACTURING PROCESS IN RUBBER INDUSTRY The Manufacture of rubber products generally involves the following three important steps: • Mixing of the rubber with certain chemical ingredients in the correct proportion and manner to form a rubber compound. • Providing appropriate shape to the compound similar to the product being manufactured. The shaped compound is called blank. • curing of the blank under correct temperature and pressure for a fixed time to get the product. • Large quantities of water are required during processing of rubber (mainly for washing, churning and dilution). 2.8(D) MACHINERY MANUFACTURING Machinery manufacturing is an important sector of the Indian rubber industry. This sector had a humble start in the 1950s and it gradually developed into a position to cater to almost all the requirements of the domestic industry. The growth of this sector has contributed substantially to the faster development of the NR processing and product manufacturing sectors. 2.8(F) RUBBER CHEMICALS 57

ISBN: - 978-93-88936-09-5 Chemicals manufacturing is yet another important segment of the rubber industry. This segment has grown fast since the 1960s. It now produces almost all the important rubber chemicals and additives for rubber processing and products manufacture. The segment has at present a large number of production units located in all the important rubber products manufacturing centres. 2.9 RUBBER RESEARCH INSTITUTIONS For the scientific development of the Indian rubber industry, there are number of institutions providing Research Development support services. The important institutions providing such services are listed below: • Indian Rubber Manufacturers Research Association, Mumbai. • The Rubber Research Institute of India (RRII), Kottayam. • The Indian Institute of Technology (IIT), Kharagpur (Polymer Science Department) • University of Cochin (Polymer Science & Rubber Technological Department). • National Chemical Laboratory, Pune (Polymer Division). • Regional Research Laboratory, Trivandrum. • Vikram Sarabhai Space Centre, Trivandrum. • Indian Space Research Organisation, (ISRO), Trivandrum. A number of institutions offer technical training both for new entrepreneurs and for existing units to upgrade the skills of the technical human resource . 2.10KONAM LATEX INDUSTRIES PRIVATE LIMITED (KLIP) Konam latex Industries (P) Ltd stated manufacturing surgical gloves in 1986 with installed capacity of 6.24 million pieces per year. The manufacturing units are situated on a total area of 300000 sq.ft in Nagercoil,Tamilnadu. Today the production capacity is 9 million pieces per month, growth of 1730 per cent over a period of 18 years and KLIP is the largest manufacturer of surgical gloves in India. 2.11 SUMMARY OF THE CHAPTER II. The review of literature indicates the importance of natural rubber in Kanyakumari district. The review helped in the generation of ideas formulation of hypothesis, selection of various tools for analysis and to arrive at meaningful conclusion. It can be understood from the review that this work tries to fill a few gaps in the subject of study. This chapter also explains clearly that profile of the study area, rubber industry, functions of rubber board and rubber research institutes in india. 58 ISBN: - 978-93-88936-09-5 59

ISBN: - 978-93-88936-09-5 CHAPTER - III NATURAL RUBBER PRODUCTION AND CONSUMPTION 3.1 INTRODUCTION Natural Rubber is obtained from the milky white fluid called latex, found in many plants. NR is nature’s most versatile vegetable product. It is the only natural material that is truly elastic, a property that allows it to be soft yet tough. Rubber gently but firmly holds everything together and absorbs humps and shocks’ to make our lives so much more comfortable. This special property has made rubber virtually indispensable and products made from rubber, now number in tens of thousands. NR is a stretchy, flexible and waterproof, hydrocarbon polymer which is derived from latex and drawn by incising into the bark of the rubber tree. It is refined into the usable rubber. The British planters introduced the commercial cultivation of NR. Rubber is a vital product in the life of every human being in the contemporary era. It supports the life of the mass through its diverse benefits. This material has multifarious uses and there are hardly any segments of society, which do not use rubber and based products. In our daily life, we are always involved with products made of rubber. The use of rubber products has made the life human beings in the modern age more comfortable. Right from the simple eraser to the wheels of the vehicles of all size, airplanes, and space shuttle, use of rubber is made in one form of other. From everyday articles such as rubber bands and shoes to mattresses, tyres and windshield wipers, rubber is so much a part of our lives that we take it for granted and assume that rubber has been with us for a very long life. India is one of largest producers of the plantation crops in the world because India is blessed with a rich and diverse agro climatic profile. Among the plantation crops in India, tea, coffee, and rubber have greater importance. Rubber plantation is a creation of human ingenuity and physical labour by applying agricultural practices. It exerts a profound influence on the economic and social life of the people. It provides employment to more than 4 lakh persons directly and a large number of persons in the various related activities indirectly and plays a critical role in the development of rural and hilly areas and under developed remote regions in the country. The term rubber plantation refers to all the individuals and organizations engaged in 60 ISBN: - 978-93-88936-09-5 the activities in connection with the cultivation of rubber, maintenance, operation, harvesting, processing and marketing. Rubber plantation provides the principal raw material required for manufacturing of variety of rubber products ranging from toy balloons to tyres for giant earth moving equipment’s. In India commercial cultivation of rubber was started in 1902 by European plants at Thattekad near Alwaye. The plantation was popularly known as Periyar Syndicate Rubber plantation sector in India employs nearly four lakh persons directly. It is noteworthy that good numbers of women are employed in this sector. Rubber plantation also provides a variety of ancillary products like honey, seed oil, seed cake and rubber wood. Being a tree crop, it has tremendous potential for eco-restoration. 3.2 NATURAL RUBBER Natural Rubber (NR) is the unique renewable resource of nature. Though rubber has been found in the latex over 2000 species of plant, Hevea Brasiliensis is the most important commercial source of natural rubber for reasons of high yield and low impurities. It is commonly known as rubber tree. Natural rubber is a tough material which possesses properties of plasticity, resistance to electricity, adhesiveness and elasticity. NR constitutes the basic raw material for more than 50000 different articles for everyday use. This has made rubber industry the second largest in the world next to iron and steel So NR plays an important role in the industrial and economic development of the country. 3.3.CULTIVATION The cultivation of is chief means of livelihood for millions of people in many rubber growing countries of the far east, who depend directly or indirectly on wages or profits received from the production of rubber plantation . 3.4 METHODS OF CULTIVATION 3.4.1 Preparation of land: Rubber plantations in India are mostly on sloppy and undulation hilly lands. Adequate soil conversation measure is necessary when the rubbers are planted in these areas. In south India, the month of June and July are very much suitable for planting rubber, the preparation of land should be finished before that period. The clearing operations should begin early to avoid delay in planting. In slightly undulating areas square or rectangular planting can be adopted. The lines should be taken east - west to get the maximum sunlight. In hilly areas the cutting of planting terraces is advisable to aid soil conservation making continuous terraces from the best protection against erosion. Proper drainage is essential on low lying lands and sloping sides which are less collapsible than vertical sides. 3.4.2 Planting of rubber trees: 61

ISBN: - 978-93-88936-09-5 It is essential that planting of rubber plants should be carried out during favourable weather. The planting density recommended is 420 to 455 plants per hectare. Fitting is necessary to provide favourable conditions for the growth of the young plants. After planting, these plants should be inspected at regular intervals. Vigorous shoot should be allowed to grow. Further planting may be carried out appropriately to remove side shoots developing up to about 24m from the ground level. 3. 4.3Manuring of rubber trees The Rubber plants have been found to respond well to systematic manuring which provides adequate nourishment to the plant. The extent of response for manuring depend upon several factors, the most important ones are the nature and fertility of the land. 3.4.4Intercropping: The main objectives of growing intercrops in the immature phase of rubber plantations are ancillary income generation. Intercropping is recommended in the first three years of planting as rubber canopy takes two or three years to shade the inter row areas. It is popular among the smallholders and is normally determined by the asset levels, nature of labour, alternative sources of income, relative agronomic suitability and profitability of the intercrops. Popular intercrops in India are 'nendran' banana, non-'nendran' banana, pineapple, ginger, turmeric and tapioca. The benefit-cost ratio of intercropping with banana in the first three years of rubber planting ranged from 1.51 to 1.60 lakhs, with ginger from 0.84 to 3.02 lakhs and turmeric from 1.52 to 2.47 lakhs. 3. 4.5Dry Natural Process (DNP) This natural process involves compressing the rubber at a high temperature and pressure. The plant proteins responsible for the allergy are denatured at these temperature and pressure and therefore pose a lower risk than rubber made by the NRL process. 3.4.6 Natural Rubber Latex Process (NRL) This process involves use of natural rubber latex in a concentrated colloidal suspension. This type latex contains a much greater proportion of plant proteins than latex produced by the dry natural process. Most immediate type reaction result from exposure to Natural Rubber Later Process. 3.4.7 Collection of Rubber (LATEX) The half shell of coconut is used as the collection container for the latex in south India where coconut is in plenty. But glazed pottery is aluminium or plastic cups are more common elsewhere, the cups or coconut shells are supported by a wire that encircles the tree. This wire 62 ISBN: - 978-93-88936-09-5 incorporates a spring so that it can stretch as the tree grows. The latex is led into the cup by a galvanized ‘Spout” that it has been knocked into the bark. 3. 4.8 Yield Cycle of Rubber The yield cycle of rubber involves broadly four phases. There is an initial pre-bearing phase of about seven years, followed by an early harvesting phase of about one to three years wherein yield is positive and increasing with high variability. Then comes the third phase which can be termed as peak bearing phase and it lasts for about four to 13 years wherein the yield reaches the highest level. In the last phase, there is a decline in yield. Replanting scheme has been proposed by the board and the basic objective of this scheme is to induce the growers to undertake timely replanting such that the shares of old age plants are reduced to minimum level. 3. 4.9. Marketing of rubber The rubber sheets in the store rooms are taken for sale at every Saturday evening. What is peculiar about the rubber sheet is that they assume black, brown or cream and produces a few odours? According to planters have varies cost of production of a sheet of rubber. 3. 4.10 Sale through Retailers Retail dealership in rubber sheet is a highly protectable business. One of the recent trends in the retail rubber business is that the retailers have been procuring latex which is fresh straight from the plantations and sell it to rubber factories which produce rubber tubes and gloves with the introductions of liberal trade policy the local producers of rubber sheets have come to know of the external market, where the demand of rubber sheets has gone up considerably leading to the resurgence of attractive price. Expenses required for cultivation In one acres of land 250-300 rubber trees can be grown. The number of trees may be varied from location to location. In the beginning stage of growth, water is required and cowdung should be used as basic fertilizer. Chemical fertilizer should also be used periodically for cultivation. The expenses required for cultivation of 1 acre of rubber for 1 year. It should be mentioned as below: Table 3.1 Sl. No. 2. Fertilizer i) Expenses required for cultivation Expenses 1. Plant (250 plants) Cow – dung 63 8,000 Rs. Rs. 12,500

ISBN: - 978-93-88936-09-5 ii) 3. Pigging 4. Maintaining Total Chemical fertilizer 5,000 13,000 3,000 4,000 32,500 3.5 FERTILIZERS USED FOR CULTIVATION Cow-dung is basic fertilizer for rubber. For 1 acres of land 1,500 kilogram of cowdung should be used. Chemical fertilizer should be mentioned by Nitrogen - Phosphorous Potassium Magnesium Urea - 800 grams 800 grams - 600 grams - 500grams - 400grams 3.6 VARIETY OF RUBBER The various varieties of rubber plants should be mentioned namely: i) T.G.R. ii) P.V. – 86 iii) R.R.I – 600 iv) v) 10S vi) P.V.28/sa 40S 3.7 EXPENSES INCURRED IN PRODUCTION FOR 1KG OF SHEET For producing 1kg of rubber sheet, the expenses should be incurred around Rs.47. It can be spitted in 32 rupees for tapping, 8 rupees for collecting latex, 2 rupees for formic acid 1 rupee for milling and 4rupees for Drying. Maximum amount is spent for tapping of rubber tree. 3.8 IMPORTANCE OF NATURAL RUBBER Natural rubber is the most useful material known to mankind on account of its wide range of application in everyday life. It has been the life – bold of Indian rubber goods manufacturing industry since independence. It is considered as a product not consumable by itself. It has derived demand and it is dependent heavily on the automobile and tyre manufacturing sectors. Cars do not move without rubber. Without an adequate supply of natural rubber, the wheels of the world industry will come to a grinding halt. Natural rubber 64 ISBN: - 978-93-88936-09-5 shows all the reactions of an unsaturated polymer. The reaction converts the plastic properties of raw rubber into elastic properties. Natural rubber enjoys excellent environmental image and rubber plantations are unique in many respects. Low intensity agriculture practiced in rubber plantation also helped to sustain long – term productivity of the soil and maintain an economically viable source of income for the planters in rubber growing areas of the country. The diversification of activities in natural rubber farming, harvesting, processing, value addition and trading as well as manufacturing and marketing of rubber products helps to engage large number of people from different walks of life. The rapid growth of industrial demand makes natural rubber economically viable to the cultivators. Rubber plantation is a good example with minimum environmental cost and social harm. There is perhaps no other agricultural systems have employment potential as in natural rubber farming. 3.9 NATURAL RUBBER – MAJOR END –USES • Natural rubber plays a pivotal role in the economic and industrial progress of over nation as it is a crucial and strategic raw material for many of the industrial products like tyres and tubes, foot wears, belts and hoses, battery boxes, layers etc., it is not only an important crop but also a crucial substitution for imports of the country. • Obtain from the latex of heave brasiliensis. • Native of Brazil – introduced to India in 1873. • Gestation period 6-7 years. • Economic life – around 25 years. • Commercial cultivation started in 1902. • Raw material from 35000 products in India. 3.10 PRODUCTION SECTOR Production of Natural Rubber has been increasing steadily over the years. The increase in production of natural rubber in major rubber producing countries has enabled global production quantity to increase. Spurt in manufacturing activities and high demand for automobiles has enhanced the demand for rubber and rubber products globally. Natural rubber therefore could be rightly said as an important agriculture product with significant. Production of Natural Rubber (NR) in India during the year 2015-16 fell 12.9 per cent to 562,000 tonnes from 645,000 tonnes produced a year ago. Adverse weather, high wages, lack of skilled labourers, grower’s reluctance in harvesting or maintaining trees in response to the low NR prices have affected the production of natural rubber (NR) in India during the 65

ISBN: - 978-93-88936-09-5 year ended March 2016. Even though the trappable area under natural rubber was 559,000 ha during 2015-16, only 391,000 ha has contributed to the NR production during the year. Consequently, the average yield, measured in terms of production per hectare of tapped area, declined during the year to 1,437 kg/ ha as against 1,443 kg/ha in the previous year. During 2016-17, the country is anticipated to produce 654,000 tonnes of NR up 16 per cent on year. 3.11 WORLD RUBBER SCEANERIO: The U.S.A the West Germany, France, Netherland, Australia, Canada, India, Japan and Brazil are the most important Rubber manufacturing countries in the world. The natural rubber is produced and supplied by Malaysia, Indonesia, Leyland, Vietnam, Cambodia, India, Thailand and Brazil. In 2009 Indonesia stands first with total area of 5290 lakhs hectares in the world and followed by the Thailand, Malaysia, China and India. Export of natural rubber from India for the last 10 years showed a remarkable increase. The creditability of Indian NR in international rubber market is now established in more than 45 countries there include mainly China, Malaysia, USA, Sri Lanka, Australia, EU countries etc. Quality of tapping is good in the estate sectors of India, Vietnam and Cambodia whereas it is only satisfactory in other countries. With regard to the small holdings, quality of tapping is poor in Thailand and Vietnam while in other countries it is only satisfactory. Majority of tappers are females in all countries except in India and Cambodia. In Malaysia 60 percent of the tappers are women. 3.12 GLOBAL INDUSTRIAL DEMAND FOR RUBBER The world NR economy was the immediate outcome of geographical expansion of modern world system. The improvement in the means of transport and communications helped in the evolution of a world market (Marx-1974, 363). In the later part of 19th century western imperialist countries found out the possibilities of trade in NR. Later the uses of NR increased with the scientific discoveries and technological advancement, especially with the popularity of automotive vehicles. The aggressive spatial expansion of colonialists was with the help of automobile industry. Thus after the two global wars of twentieth century NR has evolved as the leading industrial raw material in the world-economy. At first trade in Wild Rubber (hereafter WR) was the interest of merchant capital. Later corporate capital of western world took interest in plantation economy. After the decline of British hegemony and the ascension of US, corporate capital withdrew from the plantation sector. 66 ISBN: - 978-93-88936-09-5 Table 3.2 World level rubber production by various countries from 2001 to 2015 In Metric Tonnes Country 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 Total Thailand 2320 2615 2876 2984 2937 3137 3056 3090 3164 3252 3456 3512 3548 4324.0 4473.3 48744 Indonesia 1607 1630 1792 2066 2271 2637 2755 2751 2440 2736 2765 3015 3492 3153.2 3175.4 38286 Malaysia 882 890 986 1169 1126 1284 1200 1072 856 939 1048 925 956 668.1 721.5 14723 India 632 641 708 743 772 853 811 881 820 851 1070 910 778 704.5 575 11750 Vietnam 313 331 364 419 482 555 606 660 724 752 737 919 955 953.7 1017.0 9788 China Others Total Mean Std.dev Correlation 0.95 0.95 0.94 0.94 0.95 0.93 0.93 0.96 0.93 0.98 0.96 0.99 0.99 1.00 478 527 565 573 510 533 590 560 644 665 834 864 1143 840.1 794.0 10120 1020 703 729 792 806 792 783 1022 1054 1204 502 1299 1312 1471.4 1557.8 15047 7252 7337 8020 8746 8904 9791 9801 10036 9702 10399 10412 11444 12184 12115 12314 148457 1036 1048 1145 1249 642 1399 1400 1433 1386 1485 1570 1634 1740 1730.7 1759.1 21208 706 1262 1370 1355 1298 1312 1271 1266 998 1055 1037 1131 1227 1437.3 1492.9 15667 0.98 0.98 Source: NMCE Natural Rubber- 2012-13 Source: Monthly Bulletin of Natural Rubber Trends & Statistics, Published by Association of Natural Rubber Produced Countries. Rubber board bulletin 2015 67

ISBN: - 978-93-88936-09-5 From the above table, it is clear that, Thailand occupies first place 48744 tonnes in production of NR sheet in the world level followed by Indonesia at the second place with 38286 tonnes. India has occupied fourth position with 11750 tonnes in NR production. The mean production of NR sheets is higher in the year 2013 than the other study period. India is currently the sixth largest producer of NR in 2015 with a share of 4.7 percent of world production. During 2015, the output in main producing countries viz; Thailand, Indonesia, Malaysia and Vietnam increased, whereas production in China and India decreased during 2015. Global SR production during 2015 was 14.46 million tonnes as against 14.179 million tonnes in 2014, registering an increase of 2.0 percent. Further, this table also explain that the Mean, Standard deviation and Correlation, these values are slightly fluctuating during the study period. But the correlation values are shown above 0.5 percent level; there is a highly positive correlation between total world rubber production of main countries and the study period from 2001 to 2015. Year-wise Trends in World Rubber Consumption Global consumption of natural rubber is also witnessing phenomenal increase over the years. Utility of rubber as a raw material for various industrial purposes has augmented the demand for the product. Higher growth of manufacturing sector in developing countries helped the consumption of natural rubber to increase manifold. The following table explains that the World Natural Rubber Consumption from 2001-02 to 2014-15. 68 ISBN: - 978-93-88936-09-5 Table 3.3 World Rubber Consumption from 2001-02 to 2014-15 (‘000 tonnes) Year 2001-02 2002-03 2003-04 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11 2011-12 2012-13 2013-14 2014-15 TOTAL Mean Std.dev correlation Natural Rubber 7333 7567 7912 8698 9190 9662 10139 10181 9361 10773 11007 11042 11055 12137 136057 9718.357 1457 0.612 Synthetic Rubber 10253 10737 11206 11700 11735 12420 13073 12508 12117 13976 14831 14895 14902 19984 184337 13167 2487 Total Consumption 17586 18304 19118 20398 20925 22082 23212 22689 21478 24749 25838 25937 13104 32121 307541 21967 4530 0.685 Source: International Rubber Society Group, 2014, Rubber Statistical Bulletin (April – June 2016) of International Rubber Study Group The above table stated that World Rubber Consumption from 2001-02 to 2014-15. In terms of global consumption of rubber, witnessed a growth rate of 3.14 percent. Compared to the year 2000, global demand rose to about 30 percent by 2014-15. Consumption of natural rubber increased from 7333 tonnes in 2001-02 to 12137 tonnes in 2014-15, except in 2009-10. In the case of synthetic rubber also similar trend is visible in terms of increase in consumption over the years. Consumption of synthetic rubber increased from 10253 tonnes in 2001-02 to 19984 tonnes in 201415, except in 2008-09. Increase in the economic development in the emerging economies like China and India had played a significant role for the increase in the consumption of rubber both natural as well as synthetic rubber. This study reveals that, the World Rubber Consumption have the results of variables consumption of NR, and consumption of SR are more than 0.5. The shows results are 0.612 and 0.685 respectively. So it is a highly positive correlation. There is significant relationship between the total consumption of natural rubber in the world and consumption of NR, and consumption of SR. 69 Growth Rate -3.21 4.08 4.45 6.7 2.58 5.53 5.12 -2.25 -5.34 15.23 4.4 0.38 0.8 3.2 47.67 3.2 4.7

ISBN: - 978-93-88936-09-5 This table also explain that the Mean and Standard deviation. It is noted from the table that the Mean value for consumption of NR is 9718 tonnes, consumption of SR is 13167 tonnes and total consumption of rubber is 21967 tonnes. But the Standard deviation values forconsumption of NR is 1457 tonnes, consumption of SR is 2487 tonnesand total consumption is 4530 tonnes. Consumption of Natural rubber in main producing countries in the world: China and India are among the major rubber producing as well as consuming countries in the world. China remained the top consumer of natural rubber during the period from 2009 to 2015. High pace of economic development accompanied by large scale infrastructure development and increase demand for automobile enhanced the demand for natural rubber in China and India. The following table explains that, the Consumption of Natural rubber in main producing countries in the world from 2001 to 2015. 70 ISBN: - 978-93-88936-09-5 Table 3.4 Consumption of Natural rubber in main producing countries in the world from 2001 to 2015 (In thousand tonnes) Country India 20012002 20022003 20032004 20042005 20052006 20062007 20072008 851 20082009 881 20092010 China 1395 1538 2000 2266 2743 2812 2940 3460 3384 631 680 717 745 789 815 905 USA 974 1111 1079 1144 1159 1003 1018 1041 687 729 749 784 815 857 874 Japan Malaysia 401 408 421 403 387 383 Indonesia 142 145 156 196 221 355 Thailand 253 278 299 319 335 321 Rep.of Korea World total 332 326 333 352 370 364 887 450 391 374 377 878 469 414 398 358 636 470 352 399 330 20102011 944 926 750 458 421 459 384 20112012 20122013 20132014 20142015 3646 3622 3853 4760 4680 957 1029 950 772 402 441 480 402 988 1015 993 936 691 475 579 600 733 490 488 441 396 932 709 447 539 541 402 387 7333 7554 7952 8718 9200 9677 10144 10173 9325 10778 10963 11005 12137 12167 Mean 607 654 723 780 857 865 Std.dev 419 473 598 678 827 832 911 987 895 861 1034 1024 1094 998 1013 1042 1168 1167 1084 1159 1468 1434 Source: Rubber board 2014. Rubber statistical Bulletin of international Rubber study Group department of applied economics, CUSAT 71

ISBN: - 978-93-88936-09-5 It is clearly evident from the table 3.4 that the Consumption of Natural rubber in main producing countries in the world from 2001 t0 2015. Global consumption of natural rubber increased from 9.3 million tonnes in 2009 to 11 million tonnes in 2012. The increase in consumption of natural rubber in India also rests on the same in the calendar year of 2012 china, India and USA consumed 3.8 million, 0.98 million and 0.95 million of natural rubber according to more than 50 percent of the total global consumption of natural rubber. India ranks second with regard to NR consumption in 2015 with a share of 8.2 percent of world consumption. Consumption of NR in China, India and Japan decreased by 1.7, percent 2.1 percent and 2.5 percent respectively during 2015 on year. NR consumption in USA showed a small positive growth of 0.5 percent during 2015 on year. The above table shows that output of the Mean and Standard deviation. It is noted from the table that the mean and standard deviation values are gradually increasing trend. Consumption too has been rising steadily during the study period. The highest mean score of Consumption of Natural rubber in the world for the study period registered is 1168 tonnes in 2014. Type-wise Production of Natural Rubber in India More than 70 percent of the natural rubber in the country is produced in the form of RSS grades. Production of RSS grade rubber in the country increased from 454180 MT in 1999-00 to 642555MT in 2014-15. Solid Block Rubber is another important form of natural rubber produced in the country. The following table explain the Type-wise Production of Natural Rubber in India. 72 ISBN: - 978-93-88936-09-5 Table 3.5 Type-wise production of NR in India (metric tonnes) Latex year 2001-02 2002-03 2003-04 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11 2011-12 2012-13 2013-14 2014-15 TOTAL Mean Std.dev Correlation RSS Grades 453465 441875 490070 532155 566445 612735 583825 617125 578650 618960 658200 667225 622540 642555 8085825 577559 72852 0.999 Concentrate (DRC) 62990 76205 81860 78795 90950 86780 88305 88070 85760 76055 76490 73150 68075 67815 1101300 78665 21970 0.21 Solid Block Rubber 65665 81405 87665 84275 92540 98500 100705 110275 120780 117830 119815 122125 106815 107520 2006900 143350 147690 -0.5 Others 49295 49950 52055 54440 52690 54880 52460 49030 46210 49095 49195 51200 46570 45655 702725 50195 13260 -0.17 Source: Rubber Board Statistics, Various Issues, 1999-2014 The above table shows that, the Type-wise Production of Natural Rubber in India, RSS grade rubber constitutes about 75 percent of the total natural rubber production in the country. Type-wise production of NR, include RSS grade, solid block rubber, Latex concentrate (DRC), and other rubbers are highly volatile in nature, but has fluctuated over the year. The share of solid block rubber in the total production increased from 10 percent in 2001-02 to 18 percent in 2014-15. Latex concentrate (DRC) and other forms of natural rubber occupy about 8 percent and 6 percent of the total production of natural rubber in the country in 2014-15. Co-efficient of Correlation employed to examine the, Ho: There is no significant relationship between the total production of natural rubber in the country and grade of rubber sheets, include, RSS grades, Latex concentrate (DRC),solid block rubber and others. H1: There is significant relationship between the total production of natural rubber in the country and grade of rubber sheets, include, RSS grades, Latex concentrate (DRC), solid block rubber and others. This study reveals that all the grades of rubber sheets have the results of variables are less than 0.5 except, RSS grades. The shows results are 0.999, 0.21, -0.5 and -0.17 respectively. But, the 73

ISBN: - 978-93-88936-09-5 total production of natural rubber in the country and RSS grades of rubber sheets obtain the value is 0.999. It is a highly positive correlation, then the hypothesis significant. The total production of natural rubber in the country, solid block rubber and others rubber sheets are obtain the result values are -0.5 and -0.17. It is a highly negative correlation, then the hypothesis insignificant. This table also explain that the Mean and Standard deviation. The highest mean score of Type-wise production of NR for the study period registered as RSS grades is 577559 MT. But the Standard deviation values for RSS grades is 72852, Latex concentrate (DRC) is 21970 MT, solid block rubber is 14769 MT and other grade is 13260 MT. Consumption of Rubber – Sector wise – product –wise: Indian economy had to face recurring crises such as poor harvests, industrial recession, investment slowdown resulting from plan holidays and the chronic inflation which had depressing effects on all sectors of the economy. Even in the midst of these difficulties rubber consumption advanced steadily thanks to the spectacular progress of the tyre as well as non-tyre sectors. The growth of tyre-tube-sector1 explains that the Sector wise Consumption of Rubber from 2004-05 to 2014-15. registers an annual growth rate of 14 per cent. The following table 74 ISBN: - 978-93-88936-09-5 Table 3.6 Sector wise Consumption of Rubber from 2004-05 to 2014-15 (Qty. in metric Tonnes) Rubber/sector 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11 2011-12 2012-13 2013-14 2014-15 NATURAL RUBBER Tyre sector 406226 442921 462081 495526 508121 521812 597623 631410 635539 652434 645000 349179 358189 358224 365929 363599 369935 350092 333005 337166 329086 314587 Non-Tyre sector SYNTHETIC RUBBER Tyre sector Non-Tyre sector TOTAL NR+SR Tyre sector Non-Tyre sector RECLAIM RUBBER Tyre sector Non-Tyre sector TOTAL (NR+SR+RR) Tyre sector Non-Tyre sector 75 19908 51997 21978 54557 23714 54721 27391 55774 29191 56839 30424 57439 40511 59779 43178 59257 45879 68716 49559 74166 45678 69547 557401 606479 656604 713904 722406 754206 936548 981953 1004830 1050994 1547432 495559 508661 512966 527871 528294 529236 523287 508247 526630 537826 520955 537493 584501 632890 686513 693215 713799 896037 938775 958951 1001435 1379519 442562 454104 458254 472097 471455 477879 463508 448990 457914 463660 475784 131267 141580 170809 190987 185094 191987 298414 307365 323412 349001 351247 93383 95915 100021 106168 107856 107944 113416 115985 120748 134574 124769

ISBN: - 978-93-88936-09-5 Source: Rubber Board Statistics, Various Issues, 2008-15 76

ISBN: - 978-93-88936-09-5 The above table shows that, the Sector wise Consumption of Rubber from 2004-05 to 201415. In case of tyre sector, the total rubber consumptions of NR, SR, and RR, has continuously increased from 557401 MT in 2001-02 to 1547432 MT in 2014-15, during the study period. Whereas , non-tyre sector the total consumptions rubber of NR, SR, and RR, has increased from 495559 MT in 2001-02 to 529236 MT in 2009-10, thereafter, from 523287 MT in 2010-11 to 520955 MT in 2014-15, it has been steadily fluctuating. In the total consumption, overall aggregate elasticity is relative importance to the tyre as well as the non-tyre sectors. The tyre sector was the dominant partner in the early fifties, but gradually the situation is changing in India. 3.13RUBBER CULTIVATION IN INDIA 3.13 (A)Origin of Natural Rubber in India Natural Rubber is nature most versatile vegetable product. This material has multifarious uses and there is hardly any segment of life, which does not make use of rubber based materials. Nature’s this gift is also a vital raw material having immense strategic importance. Natural rubber has a hoary past. 3.13 (B) Early commercial plantations in India In India, seeds of rubber tree were forest introduced at the Royal Botanic Gardens, Calcutta by Sir Henry Wickam in 1876. The first commercial plantation of rubber, however was started by European planters who formed the “ Periyar Syndicate” in 1902 at Thattakad near Always in the east while Travancore state for the purpose. In 1904, further developments took place particularly in Central Travancore. In that year, planting of rubber was started in “yendayar”, “Eldorado” and “Mundakayam” estates. The Government of Travancore, Cochin, Madras, and Mysore encouraged rubber cultivation by granting land to the interested growers. By 1910, Mundakayam had become the leading centre of rubber plantation in India, with an area of about 4000 ha. The United Planters Association of South India (UPASI) evinced keen interest in rubber cultivation and carried out research on various aspects which helped in further development of commercial plantation. The “Malayala Manorama” a leading Malayalam news journal also played an important role in encouraging tuber plantations in Kerala. The first local joint stock company to plant rubber was floated in 1910 under the name “Malankara Rubber and Produce co., ltd. About that time, individual local farmers also began to enter the field of rubber cultivation. "Rubber Board has also been providing technical and various other financial aids to the rubber growers in the north eastern region, where 6,000 hectares of land proposed to be brought under the rubber cultivation in the 12th Five Year Plan period (2012-17),” 77 ISBN: - 978-93-88936-09-5 India's second Industrial Rubber Park has been set up recently in Bodhungnagar in western Tripura to boost the country's elastic polymer industry. The park, a joint venture between the Tripura Industrial Development Corporation (TIDC) and the Rubber Board, is the second of its kind in the country after the rubber park in Irapuram, Kerala. 3.14INDIAN RUBBER The main rubber growing regions in India also come mostly within the world's rubber belt. In India conditions approximate to these are obtained in Andaman Islands and in Kerala in the hills of Western Ghats. These rubber growing regions are confined to the South Western region of India consisting of Karnataka, Tamil Nadu and Kerala* The rainfall in these regions is not as well distributed as in the case of the other rubber growing countries such as Indonesia and Shri Lanka. Further, a long spell of dry hot season followed by heavy monsoon is found in the rubber growing regions of India, and to that extent it affects the growth and yield of rubber trees. Rubber plantations in India are concentrated, to a large extent, in Kerala State and, to a smaller extent, in Tamil Nadu, Karnataka and Andaman’s. In the South, it extends from the Kanyakumari District of Tamil Nadu State to the Coong District of Karnataka State in the North and, in general, lies west of the Western Ghats. The rubber growing area may be divided into three categories:1) The High land region is mostly mountainous and consists of reserve forests and other plantation crops. 2) The middle or lower upland region consists of small hilly areas of varying heights and sizes and the resultant valleys. 3) The lowland region denotes the flat alluvial and sandy tracts along the Arabian Sea coasts. In India, seeds of rubber tree were first introduced at the Royal Botanic Gardens, Calcutta by Sir Henry Wickam in 1876. Rubber plantations were first started in Kerala as early as 1905. Besides Kerala, Tamil Nadu and Karnataka also have rubber plantations. Rubber tree requires a hot climate and a heavy rainfall. It grows to a great height, developing huge buttresses or prop roots. Our familiar greenhouse plant resembles young wild plant. In India, the peak season for harvesting rubber is from October to January, while the lean period is during monsoon. In this condition, rubber is being cultivated in more than 17 states including Kanyakumari, Kerala, Karnataka, Tripura and Meghalaya. Rubber cultivation in the areas creates reformation and employment opportunities at the social and economic level. In 1978, India was net importer since the production was unable to catch up with the increasing demand. There are 4,500 industries including tyre and tube factories use rubber as raw materials. Now in India nearly 8, 50,000 tonnes per year rubber has been produced. Every year in India, the farmers earn income more than Rs. 8,500 crores through production of rubber. Also rubber based factories such as tyre and tube production including various industries earned thousands crores. 78

ISBN: - 978-93-88936-09-5 DOMESTIC SCENARIO Production and Consumption Trend of NR The NR production in the country is not sufficient to meet the growing consumption requirements of the domestic manufacturing industry. To ascertain the significant difference between, production and consumption of NR in India from 2001-02 to 2014-15, paired sample and correlation are administered. The resultant mean and standard deviation are also presented under the table Table 3.7 Year 2001‐02 2002‐03 2003‐04 2004‐05 2005‐06 2006‐07 2007‐08 2008‐09 2009‐10 2010‐11 2011‐12 2012- 13 2013- 14 2014-15 Total Mean Std.Dev Correlation Paired sample 2.4 Production (in tonnes) 631,400 649,435 711,650 749,665 802,625 852,895 825,345 864,500 831,400 861,950 903,700 913,700 844000 645000 11087265 791948 97112 0 .589 2tailed 0 .31 NA Source: NMCE Natural Rubber- 2014-15. From the above table, it is clear that, there is a positive relationship between production and consumption of NR. India’s natural rubber production has been increasing steadily over the past decade. The production grew at the rate of 4.8 per cent in the year 2011‐12 and it decline gradually from the year 2013-14 till the study period. Consumption too has been rising steadily. It grew at the rate of 2.8 per cent in 2014‐15. Consumption grew from 525 thousand tonnes in 1995‐96 to 1021 thousand tonnes in 2014‐15. The above table shows that output of the Mean and Standard Deviation. It is noted from the table 3.7 that the mean value for the production of rubber is 791948 tonnes, and consumption of rubber is 855790 tonnes and the standard deviation value are 97112 tonnes and 120512tonnes respectively. 79 Production and Consumption Trend of NR Production Growth rate 0.2% 2.9% 9.6% 5.3% 7.1% 6.3% ‐3.2% 4.7% ‐3.8% 3.7% 4.8% 3.7% -4.5% -3.8% Consumption (in tonnes) 638,210 695,425 719,600 755,405 801,110 820,305 861,455 871,720 930,565 947,715 964,415 972,705 981,520 1020910 11981060 855790 120512 Consumption Growth rate 1.1% 9.0% 3.5% 5.0% 6.1% 2.4% 5.0% 1.2% 6.8% 1.8% 1.8% 2.0% 2.8% 2.8% ISBN: - 978-93-88936-09-5 Further, the table explains that correlation value is 0.589, there is a positive correlation between production and consumption of NR in India, and also analysed with paired sample technique, value is 2.396, not significant for two tailed test is 0.31 and there is no relationship between production and consumption of NR in India. 3.15 STATE‐WISE PRODUCTION OF NATURAL RUBBER The rubber growing areas of the country are divided into three zones, viz., (i) Traditional region comprising Kerala state and Kanyakumari District of Tamil Nadu, (ii) Non-traditional region comprising all states other than Kerala state and Kanyakumari District of Tamil Nadu and North East region and (iii) North-Eastern region comprising Assam, Tripura, Meghalaya, Nagaland, Manipur, Mizoram and Arunachal Pradesh. 3.15.1 Traditional Regions: Rubber cultivation in India has been traditionally confined to hinterlands of southwest coast, mainly in Kanyakumari district of Tamil Nadu and Kerala. Kerala and Tamil Nadu together constitute the traditional rubber growing regions in the country. Kerala alone contributes 89 per cent of the total rubber produced in India and an area of 534,228 ha under rubber plantations. Tamil Nadu contributes another 3 per cent of the total natural rubber production. 3.15.2 Non- traditional region: These Konkan Region of are hinterlands of coastal Karnataka, Goa, Maharashtra, Orissa the hinterlands of coastal Andhra Pradesh and north-eastern states, rubber is Andaman and Nicobar Islands etc., where now being grown. The North‐Eastern region contributes up to 5per cent of the total production, while Karnataka contributes 3per cent. An area of 113,685 ha is covered by rubber plantations in the north‐eastern region In recent years among non‐traditional region, Tripura has become one of the most thrust areas for Rubber growing because of its well acceptance worldwide. In fact, Tripura was declared the "Second Rubber Capital of India" by the Rubber Board. India has succeeded in Rubber cultivation due to research and extension works undertaken by the Rubber Board. 3.16 STATE-WISE AREA AND PRODUCTION OF NATURAL RUBBER IN INDIA: The production of rubber is high in the state Kerala; they have produced 770580 tonnes with a per cent share of 75.08. It signifies that the Kerala is dominated in NR production in India due to favourable climate, skilled labours, soil condition, rubber board give advice to growers and marketers from time to time, Government support, Rubber Techno Park, Rubber Research Institute etc., and Kerala has the right to determine the price of the rubber products. 80

ISBN: - 978-93-88936-09-5 3.17 RUBBER CULTIVATION IN TAMILNADU Tamil Nadu ranks second in the production of natural rubber in the country and the credit goes to the Kanyakumari district (i.e.) nearly 90 percent of the rubber plantations are located in Kanyakumari district. It is the traditional Rubber growing region in the country. Since the entire plantations in the state is in this district. The district is well endowed by soil and climatic conditions which are best suited for rubber cultivation. Kerala and Kanyakumari district of Tamil Nadu are the traditional rubber growing regions in the country. Up to 50 per cent rubber plantations were under private enterprise 3.18 RUBBER CULTIVATION IN KANYAKUMARI DISTRICT Rubber production has been started in India before 110 years, in Kerala and Kanyakumari District. Rubber has been produced near the mountain regions but now a day’s Karnataka, Assam, Meghalaya including 17 states produced rubber. In this Kanyakumari and Kerala produce 90 percent of rubber production. In rubber board region there are 35,000 hectors rubber has been produced. In Kanyakumari nearly 25,000 tonnes rubber has been produced. There are 4,500 industries including tyre and tube factories use rubber as raw materials. Now in India nearly, 850000 tonnes per year rubber has been produced. In Kanyakumari district has geographical position is expected to face problems in marketing and distribution of rubber product. The Tamil Nadu government has not shown much interest in providing support for marketing rubber products produced in Kanyakumari district. In fact, a number of rubbers based industries soon after their establishment get sick even due to difficulties in marketing. The periodic changes in the supply – demand position in respect of industries. Rubber products are most important factor contributing to marketing problems. Another important factor is the seasonal decline in demand due to price hikes of the product necessitated by steep increase in the price of raw materials . Kanyakumari holds a dominant position both in areas of cultivation and distribution of NR in India. Rubber is one of the cash crops in the district. In Tamil Nadu rubber clove is cultivated only in Kanyakumari district. The quality of rubber produced in Kanyakumari is one of the best in the world and the yield per acre is also very high compared to other parts. In Kanyakumari district rubber is grown in about 16400 hectares and the estimated annual production is about 14000 tonnes. In the district rubber plantations are located in the Southern part and widespread along the Western Ghats in the three taluks namely Kalkulam, Vilavancode, and Thovalai. It has given ample employment opportunities and specifically in Keeriparai, Paraliyar, Manolodai, Chittar, Mylar, and Kallar areas. At an area of 4785.7 hectares rubber is planted in Kanyakumari district from 1960 onwards under the various schemes. The plantation and the number of the rubber growing units in Kanyakumari district are growing day by 81 ISBN: - 978-93-88936-09-5 day. The total area of Kanyakumari district is 1684 sq. Km i.e 650024 sq. Miles (168356216) ha. out of this, an extent of 48423 ha. was discovered by forests. Most of the forests in the district are in the catchment areas of numerous streams and rivers. The forests ranging from an altitude of 200 to 600 m occur in Keeriparai, Mangolamottai (Lower Kodayar) Maruthaparai beat of Kulasekharam, Ulakkaruvi near Alagiyapandipuram range Kuttiyar region of Petchiparai. Some areas have been cleared in this region for rubber plantation by the Forest Department. In the Thovalai taluk, 1038 hectares of area are under the cultivation of rubber. In Kalkulam taluk there are 2161 rubber units and the total area of rubber cultivation is 5338 hectares. In the Vilvancode taluk there exists 1468 rubber units and they are under rubber cultivation is 4931 hectares. The total number of rubber units in this district is 3668 and the total area under rubber cultivations is 11307 hectares. In Kanyakumari district contributes nearly 95 per cent of national rubber production of Tamil Nadu. The area under rubber cultivation is steadily increasing when compared to other major crops like paddy, banana, and tropica. Area under rubber cultivation has increased from 16132 hectares in 1985-86 to 16985 hectares in 1994-95. The rubber production has also gone up from 11000 tonnes in 1985-86 to 15000 tonnes in 1994-95. In June 1960, the government initiated a scheme in the Keeripparai reserve forest area and brought about 2,000 acres under rubber plantations were under private enterprise. In 1980, Kanyakumari district alone 12,688 hectares were under rubber plantation as against 12,716 hectares in the whole of Tamil Nadu. In 1980-81, 4, 39,566 rubber trees were tapped employing 3,000 persons; annual yield per tree was 5.35 kg. In 1986, the area under rubber plantations further rose to 12,168 hectares in all i.e. government as well as private, employing about 50,000 persons. Rubber plantations were widespread along the Western Ghats in Vilavancode, Kalkulam and ThovalaiTaluks and to be specific in Keeripparai, Manalodai, Chittar, Mylar and Kallar areas. In 1986 the area under Rubber plantations had further raised up to 12168 hectares in government as well as private which constitutes about 97percentage of the total area covered under plantations in the state of Tamil Nadu. In these plantations alone about 50000 people are employed. There are three factories to process the field latex into different grades of natural rubber. These factories are located at Keeriparai, Perunchani and Mylar. The number of persons employed in the Rubber Factory at Keeriparai is 103 and rubber factory at Perunchani is only 42. The total strength of labour force is in Rubber Corporation consisting of tappers, field workers, protective workers, factory workers, and casual workers. Preference is given to Kanis (hills tribes) in the recruitment of workers. Daily wages are paid to the labourers at rates varying according to their grades. Tapping are paid over kilo wages for collection of latex and scrap at the rate of 65 paise per kg and 30, paisa per kg. respectively more than the standard output. 82

ISBN: - 978-93-88936-09-5 Rubber Board which is under the control of the government of India is having a regional office at Marthandam in Kanyakumari District and a rubber nursery near Kuzhithurai necessary extension and advisory services are rendered through the regional office for the benefit of rubber growers in Kanyakumari District with the object of increasing production and productivity of rubber and on the other hand to uplift their economic condition. The board implemented “Rubber plantation Department scheme” with a view to encourage new planting, replanting during the five year period from 1980-81 to 1984-85. The scheme has provided for the grant of materials, finance and technical assistance from the board and also long term loans from bank under the credit scheme of agricultural refinance and developed corporation to the rubber growers. The rubber plantation industry is operative both in private and government owned plantations. The private estates are owned and managed by individual entrepreneurs. The Government Rubber plantations in the district have been under the control of Tamil Nadu forest department till 30.09.1984. These were converted into a company called Arasu Rubber Corporation Ltd, Nagercoil.registered under the company’s act of 1956 on 10.08.1984. Nearly 1/3 of the total rubber production of the district is now made by this corporation. The rubber board has launched various incentive programmes to bring additional areas In Kanyakumari district factories are running in Nagercoil, Kulaseharam, Karodu, Ponmanai and Thuckalay produces clouse, slippers, balloons, rubber band etc., used rubber as raw mate Rubber is most important cash crops production made by India. The Kanyakumari district rubber grower’s co-operative marketing society in engaged in the marketing of rubber. During the year 1982-83 the society handled rubber to the extent of Rs.47.69 lakhs. The society also undertakes other service functions such as distribution of inputs and requisites like manures, formic acid, aluminium paws, rubber kates, tapping knives, digging forks etc. to its grower members. The best quality latex is produced in Kazhiyal and Kulasekharam rubber plantations in Kanyakumari district. Out of 91807 hectares of total crop area, rubber is cultivated in 19500 hectares. Nearly 24000 tonnes of natural rubber is produced per annum. There are 16 rubber estates with an area of more than 20 hectares 24 rubber estates with an area of 10 to 20 hectares and growing rubber in 6011 hectares and about 30250 small holdings in 13489 hectares.16Arasu Rubber Corporation cultivates rubber in 4280 hectares. There are thirty five Rubber Producers Societies in Kanyakumari district. They give training to the members, conduct seminars, supply planting materials and provide technical advices. The Rubber Board provides loans and subsidies to the growers through this RPS. There are fifteen SelfHelp-Groups engaged in rubber honey production and rubber nurseries. Moreover there are 250 registered rubber dealers in the district. Thus the district has all the factors favouring rubber 83 ISBN: - 978-93-88936-09-5 plantation. The value of rubber latex can be enhanced by manufacturing rubber products. In Kanyakumari district there were 126 small scale rubber-based industries registered under the District Industries Centre (DIC) with a capital of Rs.437 lakh. They manufactured rubber products like gloves, rubber balloons, rubber bands, rubber sheets and mats. They provided employment to 1874 people. But most of them have downed their shutters. Total Area, Production and Productivity in (kg) yield/ha The following table shows that the total area, production and Productivity in (kg) yield/ha. To ascertain the significant difference between the total area and yield per hectare, “T” statistics is administered. The resultant mean, standard deviation, correlations are presented in table 3. 8 Table 3.8 Year 2001-02 2002-03 2003-04 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11 2011-12 2012-13 2013-14 2014-15 Total Mean Std.dev t-test Correlation Total Area, Production and Productivity in(kg)yield/ha Total Area (ha) 566555 569667 575980 584090 597610 615200 635400 661980 686515 711560 737000 757520 844000 816455 9359532 668538 92958 32 0.51 Source : Rubber Growers Guide, 2014 5% level of significance Table 3.8 shows that Total Area, Production and Productivity in (kg) yield/haRubber from 2001-02 to 2014-15. The production of rubber has increased from 631400 tonnes in 2001-02 to 852895 tonnes in 2006-07 and 2007-08 onwards the rubber production and average yield are fluctuating. We understood from the study there is a close relationship between production and average yield. It is clear from table that the average yield of rubber has also increased from 1576 kg in 2001-02 to 1879 kg in 2006-07. From the above table, indicates that there is a positive relationship between area in hectors and production in tonnes since increase in area results increase in production. 84 Production (Tonnes) 631400 649435 711650 749665 802625 852895 825345 864500 831400 861950 903700 913700 844000 645000 11087265 791948 97112 30 Productivity in(kg)yield/ha 1576 1592 1663 1705 1796 1879 1799 1867 1775 1806 1841 1813 1629 1236 23977 1713 170 59

ISBN: - 978-93-88936-09-5 It signifies that the total rubber area is increased and automatically it affects the other variables like tapped rubber area, production and average yield/ha also increased. In the year 2013-14 and 2014-15 the rubber area were decline due to the natural calamities (heavy rain) also affect the total production are 844000 and 645000 tonnes respectively. The above table shows that output of the Mean and Standard Deviation. It is noted from the table that the mean value for Total Area (ha) the of rubber is 668538, production of rubber is 791948 tonnes, and Average yield/ha (kg) of rubber is 1713and the standard deviation value are92958 ha, 97112 tonnes and 170 ha/kg respectively The significant difference among the variables of NR in India are identified Total Area (ha), Production (Tonnes) and Average yield/ha (kg) as the respective “T” statistics are statistically significant at 5 per cent level. SOCIO ECONOMIC STATUS OF NR GROWERS OR MANUFACTURES IN KANYAKUMARI DISTRICT, ANALYSIS: DISTRIBUTION OF SAMPLE RESPONDENT BY AGE Age is one of the decisive factors which decide the nature of rubber growers, distributors, and vendors. Age gives less inducement to the virtues of thrift and hard work and thereby hinders development. The following table explains the different age group of the respondents. Table 3.9 Taluk Kalkulam Vilavancode Thovalai Total Age wise classification Age wise classification 20-30 10 (7.1) 15 (13.6) 5 (10) 30 (10) 30-40 50 (35.7) 20 (18.2) 15 (30) 85 (28.3) 40-50 40 (28.6) 25 (22.7) 10 (20) 75 (24.9) Source: Computed from Primary data Note: Figures in parentheses indicate the percentage to total Table3.9 reveals that the respondents who are below 60 years are 255 respondents and above 60 years, are 45 respondents. Majority of the respondents are in the age group between 3040 years. There are 190 respondents belonging to the age group of 20-50 years distributed as 100 from Kalkulamtaluk, 60 from Vilavancodetaluk, and 30 from Thovalaitaluk. There are 110 85 50-60 25 (17.9) 30 (27.3) 10 (20) 65 (21.6) above 60 15 (10.7) 20 (18.2) 10 (20) 45 (14.9) Calculated χ2 Value 35.4 < Table χ2 Value 3.84 at 5 % level. Total 140 (100) 110 (100) 50 (100) 300 (100) ISBN: - 978-93-88936-09-5 respondents belonging to the age group of above 50 years distributed as 40 from Kalkulamtaluk, 50 from Vilavancodetaluk, and 20 from Thovalaitaluk. Ho: There is no significant relationship between the Age wise classification and three taluks. H1: There is significant relationship between the Age wise classification and three taluks. The calculated value is (35.4) more than the table value is (3.84). Hence, the hypothesis is rejected. Therefore, it is concluded that there is significant relationship between the age wise classification and three taluks. DISTRIBUTION OF SAMPLE RESPONDENT BY SEX Sex includes male and female. It is a determining factor of both employments and unemployment. Family includes more working class people, since their earning capacity is high. The following table explains the sex wise classification in the selected three taluks. Table3.10 Sexwise Classification Male Female Total Sex Kalkulam % Vilavancode % Thovalai % Total % 64 36 90 50 140 100 80 30 110 73 27 100 40 10 50 80 20 100 Calculated χ2 Value 48.6 < Table χ2 Value 3.84 at 5 % level. Source: Computed from Primary data Note: Figures in parentheses indicate the percentage to total Table 3.10 shows that the total respondents are 300 of which male respondents are 70 per cent (210) and female respondents are only 30 per cent (90). Majority of the respondent are male. They are dominating NR cultivation. Among the male respondents are 64 per cent (90) belongs to Kalkulam taluk, 73 per cent (80) respondents are belongs to Vilavancode taluk and 80 per cent (40) belongs to Thovalai taluk. . Among the female respondents are 36 per cent (50) belongs to Kalkulam taluk, 27 per cent (30) respondents are belongs to Vilavancode taluk and 20 per cent (10) belongs to Thovalai taluk. Chi-square test was employed to examine the, Ho: There is no significant relationship between the sex wise classifications in the selected three taluks. H1: There is significant relationship between the sex wise classifications in the selected three taluks. 210 70 90 30 300 100 86

ISBN: - 978-93-88936-09-5 The calculated value is (48.6) more than the table value is (3.84). Hence, the hypothesis is rejected. Therefore, there is significant relationship between the sex wise classifications in the selected three taluks. DISTRIBUTION OF SAMPLE RESPONDENT BY LITERACY Education is the apprenticeship of life. Education is more powerful tool for shaping and moulding every person’s life. It is basic necessity for social awareness. Education brings out a better society. Lack of education prevents social and economic enrichment. The educational status of the sample respondents are depicted in the following table. Table3.11 Education wise classification Literacy Kalkulam % Vilavancode % Thovalai % Total Upto elementary Higher secondary Graduate Technical Professional Illiterate Total 50 34 30 11 5 10 140 36 24 21 9 3 7 100 45 25 15 5 8 12 110 41 23 13 5 7 11 100 15 13 7 5 3 7 50 30 26 14 10 6 14 100 110 72 52 21 16 29 300 Source: Computed from Primary data Note: Figures in parentheses indicate the percentage to total Table 3.11indicates that , the respondents who are the up to elementary level are 110, higher secondary level are 72, graduate are 52, technical level are 21, professional level are 16 and illiterate are 29 in the total sample size. The majority of the respondents have educational level ranging from primary to graduate. In Kalkulam taluk 130 respondents are educated, and 10 are illiterates. Vilavancode taluk 98 respondents are educated, and remaining 12 are illiterates. In Thovalai taluk 43 respondents are educated, and 7 are illiterates. DISTRIBUTION OF SAMPLE RESPONDENT BYMARITAL STATUS Rubber growers also depend upon marital status. Married women with children enjoy some privileges’ in the family than others. In recent times, there has been a trend towards nuclear families, which means that the young married women are no longer under the direct control of their in-laws. Hence, an attempt is made to classify the sample respondents based on their marital status. The following table explains the marital status of the respondents. 87 ISBN: - 978-93-88936-09-5 Table3.12 Kalkulamtaluk Status Married Unmarried Others Total 35 20 140 Marital status wise classification Vilavancodetaluk 61 24 15 70 25 15 64 23 13 Thovalaithovalai Respondent % Respondent % Respondent % Total 85 30 15 5 100 110 100 Source: Computed from Primary data Note: Figures in parentheses indicate the percentage to total The table 3.12 stated that 62 percent (185) of the respondents are married 25 per cent (75) of the respondents are unmarried and 13 per cent (40) of the respondents are widows and divorce. Majority of the respondents are married, they have more responsibility and influence to do for earnings. Among the Kalkulam taluk, 61 per cent (85) of the respondents are married, 24 per cent (35) of the respondents are unmarried, and 15 per cent (20) of the respondents are others. In Vilavancode taluk 64 per cent (70) of the respondents are married, 23 per cent (25) of the respondents are unmarried, and 13 per cent (15) of the respondents are others. In Thovalai taluk, 60 per cent (30) of the respondents are married, 30 per cent (15) of the respondents are unmarried, and 10 per cent (5) of the respondents are others. Chi-square test was employed to examine the Ho: There is no significant relationship between the marital statuses in the selected three taluks. H1: There is significant relationship between the marital statuses in the selected three taluks. The calculated value is (104) more than the table value is (5.99). Hence, the hypothesis is rejected. Therefore, there is significant relationship between the marital statuses in the selected three taluks. DISTRIBUTION OF SAMPLE RESPONDENT BY CATEGORIES Based on the Natural Rubber cultivation, the cultivators are classified into full time cultivators and part time cultivators. The information regarding categories of rubber cultivators are given below. Table3.13 88 50 60 30 10 Calculated χ2 Value 104 < Table χ2 Value 5.99 at 5 % level. 185 75 40 100 300

ISBN: - 978-93-88936-09-5 Category Full time Part time Total Kalkulam taluk 90 50 140 Percentage 64 36 100 Categories wise classification Percentage Vilvancode taluk 75 35 110 68 32 100 Thovalai taluk 35 15 50 Percentage Total 70 30 100 200 100 300 Source: Computed from Primary data Note: Figures in parentheses indicate the percentage to total The above table reflects that 66 per cent (200) respondents are full time cultivators and other 44 per cent (100) comes under the category of part time cultivators. It reveals that Majority of the respondent is full time cultivators and their earnings are depending on NR cultivation. Among the 200 respondents are full time Cultivators. 64 per cent (90) belongs to Kalkulam taluk, 75 per cent (68) respondents are belongs to Vilavancode taluk and 70 per cent (35) belongs to Thovalai taluk. Among the 100 respondents are part time cultivators. 36 per cent (50) belongs to Kalkulam taluk, 32 per cent (35) respondents are belongs to Vilavancode taluk and 30 per cent (15) belongs to Thovalai taluk. DISTRIBUTION OF SAMPLE RESPONDENT BY USING TYPE OF FERTILIZER The continuous application of fertilizers was that it removed the primary nutrients from the soil and wide multi-nutrient deficiencies. Therefore the combined use of non-organic, organic and bio-fertilizers to maintain soil fertility. The fertilizer requirements of rubber, vary considerably during the three important stages of growth, namely nursery, immature and mature stages. The following table explains by the respondents using type of fertilizer. Table 3.14 Taluk KALKULAM VILAVANCODE THOVALAI Total Type of fertilizer Fertilizer Organic 40 (28.6) 30 (27.3) 15 (30) 85 (28.3) Non-organic 30 (21.4) 25 (22.7) 10 (20) 65 (21.6) Source: Computed from Primary data Note: Figures in parentheses indicate the percentage to total Both 70 (50.0) 55 (50) 25 (50) 150 (49.5) Total 140 (100) 110 (100) 50 (100) 300 (100) 89 ISBN: - 978-93-88936-09-5 Table 3.14 shows that 50 per cent (150) of the respondents are using both types of fertilizers, 28 per cent (85) are using organic, and rest of 65 respondents (22) are using non-organic fertilizers for cultivation of rubber. Among the organic fertilizers are used by the respondents which constitute 28.6 per cent, 27 per cent, and 30 per cent belongs to Kalkulam taluk, Vilavancode taluk, and Thovalai taluk. Among the non-organic fertilizers are used by the respondents which constitute 21per cent, 23 per cent, and 20 per cent belongs to Kalkulam taluk, Vilavancode taluk, and Thovalai taluk. It signifies that, among the both fertilizers are used by the respondents which constitute 50 per cent for all taluks in this district. DISTRI BUTION OF SAMPLE RESPONDENT BY THE PREFERENCE FOR NR CULTIVATION There are so many reasons for prefer NR cultivation. The factors include self-interest, climate, land family cultivation, unemployment, easy marketable, attractive price etc., if the absence of NR cultivation, without exist many business like tyre, slippers, cloves, lays, rubber ball, rubber band, condoms’ these product manufacturing will be suffer. The following table explain the preference for NR cultivation. Table 3.15 Preference for NR Cultivation Preferences Taluk Kalkulam Vilavancode Thovalai Total Selfinterest/ climate 10 (7.2) 10 (9) 4 (8) 24 (7.8) Family cultivation/ Land 40 (28.6) 40 (36.3) 15 (30) 95 (31.5) Unemployment 30 (21.4) 30 (27.3) 11 (22) 71 (23.6) Easy marketable 20 (14.3) 20 (18.2) 6 (12) 46 (15.3) Attractive price 40 (28.6) 10 (9.1) 14 (28) 64 (21.3) Total 140 (100) 110 (100) 50 (100) 300 (100) Source: Computed from Primary data Note: Figures in parentheses indicate the percentage to total Table 3.15 reveals that the opinion given by the five categories of NR Growers for the cultivation of rubber. Out of them 32 per cent (95) of growers’ opinion that family cultivation/availability of land. Rubber crop is give employment opportunity to unemployment candidate, according to 23.6 per cent (71) of growers, 21 per cent (64) of growers opinion that attractive price, 15 per cent (46) of growers opinion that easy marketable, and the remaining 8 per cent (24) of the growers opinion that self-interest/climate are the next suggestion. Most of the 90

ISBN: - 978-93-88936-09-5 respondent feels that the preference for rubber cultivation is being availability of land and family cultivation. DISTRIBUTION OF SAMPLE RESPONDENT BY NAME OF INTERCROPPING Rubber is planted at a wide spacing and hence sufficient land and light is available in the interred areas during the initial years for intercropping. Intercrops should be selected based on the land, light availability in the plantation and marketability. The following table explains the most common intercrops are listed Table 3.16 Taluk Kalkulam Vilavancode Thovalai Total Kinds of Intercropping Intercropping Vegetables 30 (21.4) 25 (22.7) 3 (6) 59 (19.6) Fruits 60 (42.9) 55 (50.0) 15 (30) 130 (43.3) Flowers 15 (10.7) 10 (9.1) 25 (50) 50 (16.6) Source: Computed from Primary data Note: Figures in parentheses indicate the percentage to total The above table exhibits that 43 per cent (130) have fruits, 21 per cent (62) respondents have spinach 19 per cent (59) have vegetables and least 17 percent (50) have flowers as intercrops to rubber trees. It is understood that the Majority of the respondent are preferred intercrops as fruits. Among the Kalkulam taluk, 43 per cent (60) have fruits, 25 per cent (35) respondents have spinach 21 per cent (30) have vegetables and least 11 percent (15) have flowers as intercrops to rubber trees. Among the Vilavancode taluk, 50 per cent (55) have fruits, 23 per cent (25) respondents have spinach, 18 per cent (20) have vegetables and least 9 percent (10) have flowers as intercrops to rubber trees. Among the Thovalai taluk, 50 per cent (25) have fruits, 30 per cent (15) respondents have spinach, 14 per cent (7) have vegetables and least 6 percent (3) have flowers as intercrops to rubber trees. Distribution of sample respondent by Mode of Cultivation Based on the cultivation and marketing of the rubber cultivators are classified into Full time cultivators and Part time cultivators. The information regarding categories of rubber cultivators are given below. Table 3.17 91 Spinach 35 (25.0) 20 (18.1) 7 (14) 62 (20.6) Total 140 (100) 110 (100) 50 (100) 300 (100) ISBN: - 978-93-88936-09-5 Taluk Kalkulam Vilavancode Thovalai Total (64.3) 75 (68.2) 35 (70) 200 (66.6) Mode of Cultivation Mode Fulltime cultivator 90 Part-time cultivator 50 (35.7) 35 (31.8) 15 (30) 100 (33.4) Total 140 (100) 110 (100) 50 (100) 300 (100) Source: Computed from Primary data Note: Figures in parentheses indicate the percentage to total The above table inferred that 66.6 per cent (200) of respondents are full time cultivators and remaining 33.4 per cent (100) are part time cultivators. It signifies that Majority of the respondents are cultivating as on full time basis because of they are educated but unemployment and cannot do or concentrate other. In Kalkulam taluk 64 per cent (90) are cultivating as NR on full time basis, and remaining 36 per cent (50) are as on part time basis. In Vilavancode taluk 68 per cent (75) are cultivating as NR on full time basis, and remaining 32 per cent (35) are as on part time basis. In Thovalai taluk 70 per cent (35) are cultivating as NR on full time basis, and remaining 30 per cent (15) are as on part time basis. Distribution of sample respondent by Quality of Natural Rubber People are always choosing for quality product. Quality is an important aspect to sell a product. Quality product will be preferred by all sectors of people. Here, quality of the product could be classified into three cases, like Very well, Normal, below normal. The information regarding quality of rubber based on the sample respondents are given in the following table. Table 3.18 Taluk KALKULAM VILAVANCODE THOVALAI Total Quality of Natural Rubber Quality of Natural Rubber Normal 40 (28.6) 35 (31.8) 40 (80) 115 (38.2) Source: Computed from Primary data 92 Very well Below normal 80 20 (57.1) 60 (54.5) 7 (14) 147 (38.9) (14.3) 15 (13.6) 3 (6) 38 (12.6) Total 140 (100) 110 (100) 50 (100) 300 (100)

ISBN: - 978-93-88936-09-5 Note: Figures in parentheses indicate the percentage to total Table3.18shows that the 38.9 per cent (147) are feels that quality of rubber is very well, 38.2 per cent (115) are feels that the quality of rubber is Normal, and rest are 38 (12.6) per cent feels that quality of rubber is below normal. It is signifies that the Majority of the respondent feels that the quality of rubber produced in Kanyakumari district is one of the best quality in the world and the yield per acre is also very well compared to the other parts of India. Among the Kalkulam taluk, 57 per cent (80) are feels that the NR is very well, 28.6 per cent (40) are feels that normal and rest of the respondents 20 (14.3) per cent feels as below normal. Among the Vilavancode taluk, 54.5 per cent (60) are feels that the NR is very well, 31.8 per cent (35) are feels that normal and rest of the respondents 15 (13.6) per cent feels as below normal. Among the Thovalai taluk, 80 per cent (40) are feels that the NR is very well, 14 per cent (7) are feels that normal and rest of the respondents 6 (3) per cent feels as below normal. Distribution of sample respondent by Methods of Drying Drying of the crumbs, pellets or granules produced in all the new processes is carried out at about 100 c. Drying time depends upon the size of particles. Usually 4 to 8 hours are required for complete drying. The tunnel drier commonly used consists of a movable tray fitted under a stationary hood which contains an air circulating duct fan and heat exchanger. The following table explain methods of drying of rubber sheets. Table 3.19 Methods of drying rubber sheets Methods Taluk Kalkulam Vilavancode Thovalai Total Sun drying 37 (26.4) 30 (27.3) 10 (20) 77 (25.6) Partial sun drying 28 (20.0) 18 (16.4) 7 (14) 53 (17.6) Kitchen drying 10 (7.1) 8 (7.3) 12 (24) 30 (10) Smoke house 65 (46.4) 54 (49) 21 (42) 140 (46.5) Total 140 (100) 110 (100) 50 (100) 300 (100) Source: Computed from Primary data Note: Figures in parentheses indicate the percentage to total Table3.19 shows that 46.5 per cent (140) are used smoke house, 26 per cent (77) are used sun drying, and 18 per cent (53) are used partial sun drying and least, 10 per cent (30) are used kitchen drying for rubber sheets. Minimum number of respondent are using kitchen drying for rubber sheets, because of they are having less than 2 acres of land for cultivation of rubber. 93 ISBN: - 978-93-88936-09-5 Among the Kalkulam taluk, 46 per cent (65) are using smoke house, 26 per cent (37) are used sun drying, 20 per cent (28) are used partial sun drying and least, 7 per cent (10) are used kitchen drying for rubber sheets. Among the Vilavancode taluk, 48 per cent (54) are using smoke house, 27 per cent (30) are using sun drying, 16 per cent (18) are using partial sun drying and least, 7 per cent (8) are used kitchen drying for rubber sheets. Among the Thovalai taluk, 42 per cent (21) are using smoke house, 20 per cent (10) are used sun drying, 14 per cent (7) are used partial sun drying and 24 per cent (12) are used kitchen drying for rubber sheets. Distribution of sample respondent for tapping the Rubber Trees Latex is obtained from the bark of the tree by tapping. Tapping is a process of controlled wounding during which thin savings of bark are removed. The aim of tapping is to cut open the latex vessels of rubber trees tapped for the first time or remove the coagulum. The following table explains the tapping of rubber trees. Table 3.20 Tapping by the Rubber Trees Tapping Taluk Kalkulam Vilavancode Thovalai Total Yourself 10 (7.1) 10 (9.1) 10 (20) 30 (9.9) Tappers 75 (53.6) 55 (50) 15 (30) 145 (48.2) Family members 25 (17.9) 20 (18.2) 14 (28) 59 (19.6) All 30 (21.4) 25 (22.7) 11 (22) 66 (21.9) Total 140 (100) 110 (100) 50 (100) 300 (100) Source: Computed from Primary data Note: Figures in parentheses indicate the percentage to total The above table examine that the 48 per cent (145) are tapping the trees by tappers, 22 per cent (66) are tapping trees by all, 20 per cent (59) are tapping the trees by their family members and 10 per cent are tapping the trees by him/her. Among the Kalkulam taluk 53.6 per cent (75) are tapping the trees by tappers, 21.4 per cent (30) are tapping the trees by all, 17.9 per cent (25) are tapping the trees by their family members and 7.1 per cent (10) are tapping the trees by him/her. Among the Vilavancode taluk 50 per cent (55) are tapping the trees by tappers, 22.7 per cent (25) are tapping the trees by all, 18.2 per cent (20) are tapping the trees by their family members and 9.1 per cent (10) are tapping the trees by him/her. Among the Thovalai taluk 30 per cent (15) are tapping the trees by tappers, 28 per cent (14) are tapping the trees by all, 22 per cent 94

ISBN: - 978-93-88936-09-5 (11) are tapping the trees by their family members and 20 per cent (10) are tapping the trees by him/her. Distribution of sample respondents by using Type of Vessels The latex (milky rubber) that flows out is channelled into an attached container. Coconut shells and polythene cups or both used as vessels most Indian NR cultivators. Latex collected in the cups is transferred to clean bucket two/three hours after taping. The following table explains type of vessels used for collection of latex by cultivators. Table 3.21 Type of Vessels Taluk Kalkulam Vilavancode Thovalai Total Coconut 30 (21.4) 30 (27.3) 15 (30) 75 (24.9) Vessels Plastic cup 40 (28.6) 55 (50) 30 (60) 125 (41.6) both 70 (50.0) 25 (22.7) 5 (10) 100 (33.3) Total 140 (100) 110 (100) 50 (100) 300 (100) Source: Computed from Primary data Note: Figures in parentheses indicate the percentage to total The above table exhibits that the 41.6 per cent (125) are using plastic cup, 33.3 per cent (100) are using both the cups and 24.9 per cent (75) are using coconut shell for collection of latex from the rubber trees. It could be understood that majority of the respondents are using plastic cups, which are easily available in the market and has long usage. Among Kalkulam taluk, 21.4 per cent (30) are using coconut shell, 28.6 per cent (40) are using plastic cup and remaining 50 per cent (70) are using both the cups. Among Vilavancode taluk, 27.3 per cent (30) are using coconut shell, 50 per cent (55) are using plastic cup and remaining 22.7 per cent (25) are using both the cups. Among Thovalai taluk, 30 per cent (15) are using coconut shell, 60 per cent (30) are using plastic cup and remaining 10 per cent (5) are using both the cups. Distribution of sample respondent for the factors affecting production Rubber cultivation in India has been traditionally confined to narrow belt extending from Kanyakumari district of Tamil Nadu, in the south to Dakshin Kannada and Kodagu district of Karnataka and lying in general west of the Western Ghats. The climatic conditions in the rubber tract vary from region to region and from year toyear depends on rainfall. There are so many factors 95 ISBN: - 978-93-88936-09-5 affecting production of NR. The factors are classified into five like Maintenance cost, Rain/Strom, Lack of labour, tapping, and Lack of finance. The following table shows that the factors affecting production of rubber. Table 3.22 Factors Affecting Production Factors Affecting Production Taluk Kalkulam Vilavancode Thovalai Total Maintenance cost 29 (21) 25 (23) 11 (22) 65 (21) Source: Computed from Primary data Note: Figures in parentheses indicate the percentage to total The above table exhibits that 25 per cent (75) of the respondents feels that tapping, 24 per cent (71) of the respondents are feels that lack of labour, 21 per cent (65) are feels that maintenance cost, 17 per cent (53) are feels that lack of finance, and 12 per cent (36) are feels that rain/storm. It signifies that, majority of the respondents are feels that lack of availability of skilled labour and tappers. These factors might be affecting the production of rubber. Hence, in modern day’s new technology adopted for tapping and planting rubber trees. So the awareness and training should be given to them. Co-efficient of regression, for Age and sources of production In order to find out the factors that determine the age of NR growers, and the selected nine variables have been regressed on sources of production of NR by the growers’ index. The following regression equation has been framed to ascertain the impact of the variables on awareness. Co-efficient of regression, for Age and sources of production G I = where, GI a a + b1 VC+ b2 TFU+ b3 OP+ b4 OM+ b5 NOI+ b6 MOC+ b7 QOR+ b8 TOVU + b9 MOD = Growers Index = Intercept term VC = Varieties of cultivation TFU = Types of Fertilizers Used OP = Own Plantation 96 Rain/ storm 17 (12.1) 12 (10.9) 7 (14) 36 (12) Lack of labour 24 (17.2) 35 (31.8) 12 (24) 71 (24) Tapping 43 (30.7) 20 (18.1) 12 (24) 75 (25) lack of finance 27 (19.3) 18 (16.4) 8 (16) 53 (17) Total 140 (100) 110 (100) 50 (100) 300 (100)

ISBN: - 978-93-88936-09-5 OM = Own Machine NOI = Name of Intercropping MOC = Mode of Cultivation QOR = Quality of Rubber TOVU = Types of Vessels Used MOD = Methods of Drying The results of the regression analysis are shown in Table 3.23 of the nine variables taken for consideration two variables namely, varieties of cultivation and own machine in Kalkulam taluk, for consideration one variable is types of fertilizer used in Vilavancode taluk and for consideration two variables types of vessels used and methods of drying in Thovalai taluk with Others are not found to be significant. The other variables that influence the sources of the production of NR are discussed in the following paragraphs. Table 3.23 Age and Sources of production Sources of production varieties cultivation Type of fertilizers used Name of Intercropping Mode of Cultivation Quality of Rubber Type of vessels used Methods of drying ANOVAs Kalkulam taluk t-value .997 2.032 Own Plantation -2.381 Own Machine .841 4.502 -3.690 -1.318 -3.217 2.451 152.757 P value .321 .044 .019 .402 .000 .000 .190 .002 .016 .000 Vilavancode taluk t-value -1.356 .352 4.229 3.191 -1.484 2.460 2.446 -2.208 2.068 222.164 P value .178 .726 .000 .002 .141 .018 .018 .030 .009 .000 t-value 2.978 1.663 2.429 -1.741 2.938 3.610 -1.778 .355 .257 98.897 Thovalai taluk P value .005 .104 .020 .089 .005 .000 .083 .724 .798 .000 Source: primary data 5% level of significance The table 3.23 reveals that, the ANOVA value is found to be significant at five per cent level. This shows that the regression equation framed is a good fit. The ANOVA value of Kalkulam taluk indicates that around 152.757, Vilavancode taluk indicates that the value is 222.164 and Thovalai taluk indicates that the value is 98.897 of the variations in sources of the NR production due to the selected variables. 97 ISBN: - 978-93-88936-09-5 Table 3.24 Education and Sources of NR production by growers in Kanyakumari Kalkulam taluk Sources ChiVarieties of NR Type of fertilizers Own plant ation Own machine Name of intercropping Quality of rubber Types of vessels used Methods of drying Tapping of the rubber trees square 4.227 1.598 1.194 1.400 3.507 2.141 1.367 2.717 2.518 Source: Primary data Kendall's tau-b .912 .807 .647 .419 .860 .803 .777 .848 .835 Spearman Correlation .957 .883 .714 .463 .902 .855 .862 .915 .892 Chisquar e 3.371 1.100 1.100 42.778 2.400 1.643 1.633 1.753 1.989 Kendall's tau-b .901 .745 .633 .319 .812 .794 .807 .819 .834 Vilavancode taluk Spearman Correlation .944 .860 .698 .352 .858 .846 .861 .902 .890 Chisquar e Kendall's tau-b 1.435 .906 1.321 .861 50.000 15.27 .614 .320 79.39 .793 57.143 81.111 43.421 .613 .814 .674 1.001 .881 Thovalai taluk Spearman Correlation .952 .921 .685 .287 .874 .685 .884 .751 .933 98

ISBN: - 978-93-88936-09-5 This study reveals that all sources include, Varieties of NR cultivation, Type of fertilizers used, Own plantation, Own machine, Name of intercropping, Quality of rubber, Types of vessels used, Methods of drying, and Tapping the number of rubber trees have results are more than 0.5 level, it is a highly positive correlation, except own machine, shows Kendall’s tau-b results among three taluks are 0.419, 0.319, and 0.32 respectively. Whereas, Spearman Correlation results among three taluks are 0.463, 0.352, and 0.287 respectively, hence it is considered as a low positive correlation. Factors of NR Growers in Kanyakumari District The following table explains that the factors of NR growers in Kanyakumari district based on selected three taluks. The factors include Types of fertilizers used, own machine, own plantation, Varieties of intercropping, Mode of cultivation, types of vessels used, Methods of drying, Tapping of rubber trees, Preference of rubber cultivation, Quality of rubber, and smoking. Table 3.25 - Factors of NR Growers (Kendall’s co-efficient of Correlation) Kalkulam Factors Types of fertilizers used Own machine Own plantation Varieties of intercropping Mode of cultivation Types of vessels used Methods of drying Tapping of rubber trees Preference of rubber cultivation Quality of rubber Smoking .825 .395 .625 .908 .727 .847 .827 .860 .788 .912 .912 Vilavancode .765 .316. .609 .856 .710 .863 .898 .833 .572 .605 .509 Thovalai .829 .273 .578 .849 .746 .749 .643 .899 .921 .574 .675 Source: primary data 5% level of significance All the factors have more than 0.5 except own machine, shows results are .395, .316 and .273 respectively, so it has a highly positive correlation. 3.19 NATURAL RUBBER PRODUCTION Kanyakumari district is selected as the study area. The rubber is a vital product in the life of every human being in the contemporary era. It supports the life of the mass through its diverse benefits this material has multifarious uses and there are hardly any segments of society, which do not use rubber and rubber based products. In our daily life, we are always involved with products made of rubber. Production of NR in India at present is below the domestic demand, forcing the country to import NR from other rubber producing countries. An overall performance of small growers are analysed to understand their economy development in Kanyakumari 99 ISBN: - 978-93-88936-09-5 100

ISBN: - 978-93-88936-09-5 CHAPTER -IV SOCIO-ECONOMIC STATUS OF NATURAL RUBBER TAPPERS IN KANIYAKUMARI DISTRICT 4.1 INTRODUCTION Rubber tappers in Kanyakumari district face a number of problems every day. Most of the rubber tappers work on small holdings (unorganized sector) with less than 2.5 acres. It is found that most of the rubber tappers interviewed live in miserable conditions. Only a few tappers have preferred to tap trees in the rubber estates. Because of they get salary on the basis of monthly. Rubber cultivation and production are not easy and simple vocation. It needs large and professional labour force during its whole lifetime existence. Tapping of rubber trees is not only a skilled job but also a highly labour intensive. Decline in the natural rubber economy causes decline or stagnation in wage rate and reduction in the number of working days. The wage rate too is very low. The Indian penal code has stated that those who are working in industry or organisation from 10 p.m to 6a.m. get double remunerations. As per the survey, the time of tapping generally adopted 6.00 a.m to 7.30 a.m before that, they wake up in their bed. They can’t follow a proper time schedule and it is based on the availability of milky rubber. But here, tappers are tapping the trees starting from 6 a.m. There is the absence of bargaining power and trade union activities. At present, there is no serious scarcity of tappers, though there is scarcity of skilled persons. The trend shows that the younger generations are not interested in tapping, due to low wage rate system which may lead to further decline in rubber economy. Another serious problem is the shortage of skilled tappers. Most of them belong to the age limit of above 40. And majority of them are illiterate. They are not preferred to introduce new methods of tapping. 4.2 TAPPERS Tappers are those who extract rubber from the trees, while the field workers tend to the crop and provide for maintenance in the plantations. Tappers will have to be recruited when the rubber trees become fit for tapping. Generally experienced tappers are highly preferred by the plantation owners. Tapping required ‘the cutting of a very thin slice of the bark of the tree’ for collecting latex that oozes out along the edge of the cut in a cup. Tapping is not only collecting but also carrying the latex and scrap to the weighing spots. 4.3 TAPPING Latex is obtained from the bark of the rubber tree by tapping. Tapping is a process of controlled wounding during which thin shavings of bark are removed. The aim of tapping is to cut 101 ISBN: - 978-93-88936-09-5 open the latex vessels in the case of trees tapped for the first time or to remove the coagulum which blocks the cut end of the latex vessels in the case of trees under regular tapping. A sharp knife is used to cut the bark to produce several downward spirally running cuts. The tapping is done usually in the morning. The Latex that oozes out of the cuts is collected in cups placed below. Each morning fresh cuts are made to collect the latex. The bark is cut in such a manner that the delicate growth layer of cambium is not damaged. Since the latex vessels run spirally to the right at an angle of 30 of vertical plane, tapping cut is made from the upper left to the lower. First a vertical cut is made in the bark in the lower portion of the tree. Sometimes a ‘V’ cut is made. The latex which flows for several hours in the forenoon is collected in a cup. The flow of latex stops by midday and therefore, cut surfaces have to be renewed each morning for several months. Generally, tapping is a process of controlled wounding during which the shaving of bark is done. Skilled tapping is necessary for the good health of the tree and for maintaining the longevity of its production period. Similarly, response to tapping system varies from clone to clone. The Rubber Board has recommended half spiral third daily tapping for reducing the panel disease. Naturally, this system of tapping tends to reduce the number of tapping days by 30 to 35 days. Selftapping is not popular in Kanyakumari. Even Small growers of rubber area below half a hectare hire labourers for tapping. Though women tappers seemed to be efficient in tapping, the share of women in the total number of tappers is very low. 4.4 IMPORTANCE OF TAPPING Tapping laborers in rubber estates are highly organized due to the activities of trade unions. There are specified rules and regulations like Plantation Labour Act regarding wage structure, terms and conditions of work, welfare measures etc. Even though more than 10 folds of tapper in the rubber estate are working in the smallholdings; there is the absence of well-defined wage rate or working conditions. Moreover, the problems of rubber tapper in the smallholdings have not been seriously taken into consideration even by the institution connected with rubber. According to the nature of trees, there are different types of tapping system, such as daily tapping system, intensive tapping, high level tapping, and controlled upward tapping, Male tappers dominate the field of tapping. Female tappers are interested more in estate tapping because there is no gender discrimination regarding wages and allowances. . On an average a tapper tapped around 250 trees per day. Most of the respondents (tappers) are working on one holding, a few respondents are tapping trees in two or three holdings and only 10 per cent of the tappers are tapping trees in their own land. 102

ISBN: - 978-93-88936-09-5 Only 45per cent of tappers use head light for tapping. This facility is enjoyed by those who tap trees in estate holdings. The average time taken for tapping and allied works by a tapper is estimated separately. An average time of 3.25 hours is taken for tapping; 1.75 hours is taken for collection of latex and 0.50 hours is taken for rolling. Almost in all cases the drying and smoking of sheets are done by the owner of the land himself. Rubber plantations had its beginning in India during the first decade of the last century. Rubber is one of the important cash crops in the district. Kanyakumari accounts for 95 per cent of area under rubber in Tamil Nadu. In this district rubber plantations are located in the northern part of the three taluks namely Kalkulam, Vilavancode and Thovalai. It has given ample employment opportunity. 150 sample respondents (Tappers) were chosen from Kanyakumari district which include 70 tappers from Kalkulam taluk, 50 tappers from Vilavancode and the remaining 30 tappers from Thovalai taluk for the study. A good tapper can tap a tree every 20 seconds on a standard halfspiral system and a common daily, ‘task’ size is between 450 and 650 trees. Trees are usually tapped on alternate days although there are many variations in timing, length and number of cuts. The trees will drip latex for about four hours. When the flow is stopped latex coagulates naturally on the tapping cut thus blocking the latex tubes in the bark. Tappers usually rest and have a meal after finishing their tapping work. Then they start collecting the latex at about midday. Some trees will continue to drip after the collection and this leads to a small amount of cup lump which is collected at the next tapping. The latex that coagulates on the cut is also collected as tree lace. Tree lace and cup lump together account for 10-20 per cent of the day rubber produced. Latex is generally processed into either latex concentrate for manufacture of dipped goods or it can be coagulated under controlled, clean conditions using formic acid. The coagulated latex can then be processed into the higher grade technically specified block rubbers such as TSR 3L or TSRCV or used to produce ribbed smoke sheet grades. Naturally coagulated rubber is used in the manufacture of TSR 10 and TSR 20 grade rubbers. 4.5 OPERATIONAL TERMS IN TAPPING 4 .5 (A) Bark An inner layer of soft bast, an intermediate layer of hard bast, and an outer protective layer of cork cells can be distinguished in the bark of the rubber tree. Latex vessels are concentrated in the soft bast, arranged in a series of concentric rings of interconnecting vessels. The number, dimension and the distribution of latex vessels and the proportion of hard bark show much variation from tree to tree in seedling population. 4 .5 (B) Marking, slope and direction of tapping cut The tapping cut of budded trees should have a slope of about 30˚ to the horizontal. For seedling trees, the cut need to have a slope of only about 25˚, since the bark is fairly thick. A very 103 ISBN: - 978-93-88936-09-5 steep cut leads to the wastage of bark when tapping reaches the base of the tree and too flat a cut leads to overflow of latex. The latex vessels in the bark run at an angle of 3-5˚ to the right and therefore an opposite cut from high left to low right will open more latex vessels. To avoid spillage, an inward slope towards wood also has to be maintained on the tapping cut. 4 .5 (C) Standard of tapping and height of opening In India, the best period to open new areas for tapping is March-April. The trees left behind during the season due to want of sufficient girth may be opened in September. In the immature phase annual growth rate is around 7cm, whereas it will be 2cm or less under tapping. Hence, trees of lower growth than recommended should not be opened for tapping. 4 .5 (D) Tapping depths, bark consumption and bark renewal The best yield is obtained by tapping to a depth of less than one millimetre close to cambium since more latex vessels are concentrated near the cambium. Shallow tapping results in considerable loss of crop. To obtain optimum yield, care should be taken not to injure the cambium at the time of tapping. 4 .5 (E) Time of tapping, task and utensils It is necessary to commence tapping early in the morning as late tapping reduces the exudation of latex. The tapping task (number of trees tapped on a day by one tapper) in India is around 300-400 trees compared to 400-500 trees in other countries. Headlights can be used for early morning tapping during non-rainy season including summer months to extract better crop. Competing tapping with its latex flow in the early hours (02.00 to 6.00 hrs) favours better production during summer and in wind prone areas. 4 .5 (F) Tapping systems There is a response to different tapping systems varies from clone to clone. Since majority of rubber area is with high yielding clones, in general, rubber trees are to be tapped on half spiral third daily (S/2 d3) system. 1. Low frequency tapping (LFT) Trees under low frequency tapping (d3, d4, d6 or d7) have to be stimulated from opening for maximum sustainable yield. Numbers of stimulation vary with clone, age of the tree, tapping system and frequency. In high yielding clones like RRII 105 and PB 217 under third daily (d3) tapping frequency with weekly one day regular off, yield increase can be achieved by three annual stimulations (April and May, September and November. Low frequency tapping (LFT) with stimulation can be practised from the first year of tapping. 2. Controlled upward tapping (CUT) 104

ISBN: - 978-93-88936-09-5 Controlled upward tapping (CUT) can be practised from renewed panel stage onwards for longer duration of crop harvesting from the virgin bark above the basal panel. Controlled upward tapping can be adopted under the following situations: i) Low yield from the renewed bark. ii) Renewed bark is unsuitable for tapping because of outgrowths, diseases, panel dryness and the like. 3. Intensive tapping Intensive tapping is generally done on old rubber trees for a few years prior to felling. The method depends on condition of trees, previous tapping systems, availability of bark and the period available for harvesting before felling. When tapping of renewed bark on basal panels becomes uneconomic, new cuts are opened at higher levels, 180 cm from bud union or even higher. 4 .5 (G) Rain guarding During rainy season tapping can be carried out by fixing a rain guard just above the tapping cut. Thus by preventing loss of tapping days due to rain, regular tapping can be ensured by rain guarding under any given tapping frequency. Chances of bark are high when the trees are rain guarded and tapping is continued during rainy season. 4 .5 (H) Tapping rest In South India, rubber trees shed leaves from December to February and defoliate soon alongwith the production of flowers. During defoliation and flowering, the yield of rubber will be comparatively poor. 4 .5 (I) Rubber Tappers Bank (RTB) Scarcity of skilled tappers is currently the major handicap the rubber plantation industry has to address. The number of trained tappers in the job is coming down steadily and significantly. To make the tapping job attractive and to enhance the dignity of the work a scheme for formation of rubber tappers bank as an SHG of skilled tappers under the umbrella of RPS is launched. Job assurance, reasonable wage pattern, life insurance coverage, attractive savings (in PPF) and terminal benefit packages are offered to member tappers. The scheme is envisaged to ensure the availability of sufficiently skilled and motivated tappers essential for the sustainability of the rubber plantation industry. 4 .5 (J) Latex Hevea latex in the latex vessels of tapped trees contains 30-45 per cent rubber in the form of particles. Latex is a hydrosol in which the dispersed particles are protected by a complex film. It contains more than one disperse phase. 4 .5 (K) Latex flow 105 ISBN: - 978-93-88936-09-5 When a rubber tree is tapped and the vessel is cut the pressure at the location of the cut is released and viscous latex exudes. This exudation of latex results in the displacement of latex along the length of the latex vessel and laterally owing to strong forces of cohesion existing in the yield 106

ISBN: - 978-93-88936-09-5 4 .5 (L) Properties of Rubber Natural rubber is soft and translucent at 200 c, when chilled to 0-100c it becomes hard and opaque at the temperature of liquid air. It is brittle and transparent like glass. At temperatures exceeding 250c, rubber loses elasticity and becomes sticky. Rubber melts to a viscous fluid at about 2000 c. rubber is insoluble in water and is unaffected by alkalise or moderately strong acids. It is soluble in benzene, naphtha, carbon disulphide either chloroform or chlorinated hydrocarbon. Rubber is one of the best insulating and dielectric materials available. 4 .5 (M) Processing of the Crop The main crop from the rubber tree is latex, a milky white dispersion of rubber in water, which is harvested by the process of tapping. Coconut shells and polythene cups are used as containers in most of the Indian estates. Latex collected in the cups is transferred to clean buckets, two /three hours after tapping. Around 80 percent of the crop from plantation is in the form of latex. Latex is coagulated in suitable containers into thin slabs of coagulum and sheeted through a set of smooth rollers followed by a grooved set, and dried to obtain sheet rubber. Depending upon the drying method, sheet rubbers are classified into two: Ribbed smoked sheets and Air dried sheets. 4 .5 (N) Smoking The sheets, after four to six hours of dripping in shade, are put in the smoke house where the temperature is maintained between 400c and 600c. In the smoke house, sheets are dried gradually whereby blisters are avoided. It is preferable to smoke the sheets on the first day at a low temperature. For the subsequent days the sheets are to be dried at a higher temperature and fairly low relative humidity. Four days of smoking is generally sufficient under normal conditions but during the rainy season five to six days are required for a satisfactory drying. 4 .5 (O) Drying of rubber sheets Drying of the crumbs, pellets or granules produced in all the new processes is carried out at about 100 c. Drying time depends upon the size of the particles. Usually 4 to 8 hours are required for complete drying. The tunnel drier commonly used consists of a movable tray fitted under a stationary hood which contains an air circulating duct fan and heat exchanger. 4 .5 (P) Grading The completely dried sheets are shifted to the packing shed where they are carefully inspected and graded according to the standards as per IS-15361-2003. This standard provides for six grades of rubber smoked sheets, viz RSS IX, RSS 1, RSS 2, RSS 3, RSS 4 and RSS 5. 4 .5 (Q) Packing The sheets after grading are packed in bales of 50kg. In the international market a bale weight is usually III-II kg. The grades are marked on the bales and marketed by BIS. 107 ISBN: - 978-93-88936-09-5 4.6 EVOLUTION OF TAPPING The method of crop extraction was very crude in earlier days. The Brazilians used to make multiple cuts on the tree trunk and the branches and sometimes cut down the whole tree to extract latex from the bark. This process resulted in heavy damage to the healthy trees and affected sustained crop extraction. In 1890, Henry Nicholas Ridley, Director of the Singapore Botanical Gardens, devised the basic methods of current-day tapping which involves shaving out with sharp knife thin slices of the bark to cut open the closed ends of latex vessels and letting the latex flow out. He had a group of research workers in the Singapore Botanical Gardens and prescribed optimum planting density, cover crop establishment, fertiliser application, disease control and processing of latex. 4.7 EXPORT OF RUBBER PRODUCTS Various export promotion schemes have been introduced by the Government of India from time to time to enhance export from India. The Rubber Board plays an important role in enhancing export of natural rubber (NR) by implementing various export promotion measures. The schemes are designed to provide financial as well as technical assistance to exporters. It includes the compensation of the expenses incurred on quality improvement, packing, internal transport and terminal handling. This has helped the exporters to make an entry in the international market. India’s trade depends considerably on the import and export of rubber related products. Manufactured goods remained the main item in imports of rubber products, while tyre and tube products accumulated a major share in export from India. Total Area, Tapped Area, Production and Average Yield per Hectare of Rubber The following table shows that the total area, tapped area, production and average yield per hectare of rubber. To ascertain the significant difference between the total area and yield per hectare, “T” statistics is administered. The resultant mean score, mean differences, standard deviation are presented in table 4. 1 Table 4. 1 Total Area, Tapped Area, Production and Average Yield per Hectare of Rubber Year Total Area (ha) 2001-02 2002-03 2003-04 2004-05 2005-06 2006-07 2007-08 2008-09 566555 569667 575980 584090 597610 615200 635400 661980 Tapped Area (ha) 400713 407953 427935 439720 447015 454020 458830 463130 108 Production (Tonnes) 631400 649435 711650 749665 802625 852895 825345 864500 Average yield/ha (kg) 1576 1592 1663 1705 1796 1879 1799 1867

ISBN: - 978-93-88936-09-5 2009-10 2010-11 2011-12 2012-13 2013-14 2014-15 Total Mean Std.dev Correlation t-test 686515 711560 737000 757520 844000 816455 9359532 668538 92958 0.96 31.690 46.129 29.930 Source : Rubber Growers Guide, 2014, 5% level of significance Table 4.1 shows that Total Area, Tapped Area, Production and Average Yield per Hectare of Rubber from 2001-02 to 2014-15. The tapped area of rubber has gradually increased from 400713 hectares in 2001-02 to 521653 hectares in the year 20014-15. It further shows that the production of rubber has increased from 631400 tonnes in 2001-02 to 852895 tonnes in 2006-07 and 2007-08 onwards the rubber production and average yield are fluctuating. We understood from the study there is a close relationship between production and average yield. It is clear from table that the average yield of rubber has also increased from 1576 kg in 2001-02 to 1879kg in 2006-07. From the above table, it is clear that there is a positive relationship between area in hectors and production in tonnes, since increase in area results increase in production. It signifies that the total rubber area is increased and automatically it affects the other variables like tapped rubber area, production and average yield/ha also increased. In the year 2013-14 onwards the tapped rubber area were decline due to the natural calamities (heavy rain) also affect the total production is 844000 tonnes in 2013-14 and 645000 tonnes in 2014-15 respectively. The highest positive correlation between total area and tapped area for the study period is registered 0.96. So it is a significant one. The above table shows that output of the Mean and Standard Deviation. It is noted from the table that the mean value for Total Area (ha) the of rubber is 668538, Tapped Area (ha) of rubber is 462834, production of rubber is 791947 tonnes, and Average yield/ha (kg) of rubber is 1712 and the standard deviation value are92958 ha, 37414 ha, 97112 tonnes and 170 ha/kg respectively. The significant difference among the variables of NR in India are identified Total Area (ha), Tapped Area (ha), Production (Tonnes) and Average yield/ha (kg) as the respective “T” statistics are statistically significant at 5 per cent level of significance. SOCIO-ECONOMIC STATUS OF TAPPING (PRIMARY DATA) Distribution of sample respondents by Mode of tapping 468480 477230 491000 504000 518000 521653 6479679 462834 37414 831400 861950 903700 913700 844000 645000 11087265 791947 97112 0.51 58.7 1775 1806 1841 1813 1629 1236 23977 1713 170 109 ISBN: - 978-93-88936-09-5 Based on the tapping of the rubber trees by the tappers, are classified into three cases, like Full time tappers, Part time tappers and both. The distribution regarding the categories of rubber cultivators is given below. Table 4. 2 Taluk Kalkulam Vilavancode Thovalai Total Mode of tapping Mode Fulltime 45 (64.3) 35 (68.2) 20 (70) 100 (66.6) Part-time 25 (35.7) 15 (31.8) 10 (30) 50 (33.4) Total 70 (100) 50 (100) 30 (100) 150 (100) Source: Computed from Primary data Note: Figures in parentheses indicate the percentage to total From the above table, it is inferred that 66.6 per cent (100) of the respondents are full time tappers and the remaining 33.4 per cent (50) are part-time tappers. It signifies that the majority of the respondents are tapping rubber trees on a full time basis because they are not willing to-do other works, only concentrating tapping. In Kalkulam taluk 64 per cent (45) are tapping rubber trees on a full time basis, and remaining 36 per cent (25) are on part time basis. In Vilavancode taluk 68 per cent (35) are tapping rubber trees on a full time basis, and remaining 32 per cent (15) are on part time basis. In Thovalai taluk 70 per cent (20) are tapping rubber trees on full time basis, and remaining 30 per cent (10) are on part time basis. Distribution of Sample Respondents on satisfactory level The level of satisfaction differs from farmers to farmers and place to place. The level of satisfaction is one of the factors which determine the preference given to the tapping. The following table shows the opinion about the satisfaction of tappers. Table 4.3 Level of Satisfaction of Tappers Satisfaction of Tappers Taluk Kalkulam Highly satisfied 14 (20.0) Total Satisfied Neutral 19 (27.1) 110 20 (28.6) Poor 10 (14.3) Very poor 7 (10.0) 70 (100)

ISBN: - 978-93-88936-09-5 Vilavancode Thovalai Total 9 (18) 8 (24.2) 31 (20.6) 9 (18) 8 (24.2) 36 (23.9) 13 (26) 12 (36.4) 45 (29.9) 10 (20) 3 (9.1) 23 (15.3) 9 (18) 2 (6.1) 18 (11.9) 50 (100) 30 (100) 150 (100) Source: Computed from Primary data Note: Figures in parentheses indicate the percentage to total The above table shows that, the opinion regarding the satisfactory level of the tappers. Of the 150 respondents, 20.6 per cent of the respondents feel highly satisfied, 24 per cent of the respondents come under satisfied category, 30 per cent of the respondents are neutral, 15 per cent of the respondents have poor satisfaction, and the remaining 12 percent of the respondents feel very poor about the tapping of the rubber trees. Majority of the respondents feel satisfied about their tapping of the rubber trees because skilled tappers are available there. Factors of NR tappers in Kanyakumari district The following table explains the factors of tappers, based on three taluks, in Kanyakumari district. The factors include Age, Sex, Education, Experience, Experience, Sources of income, Amount of income earned, Sources of finance, Advance received, Repayment of the amount, and Remuneration, are analysed with the help of the Mean score, Standard deviation, t-test and Mean Deviation. 111 ISBN: - 978-93-88936-09-5 Table 4.4 Kalkulam Factors Mean Std.dev Age Sex Education Experience t M.D Mean Std.dev t M.D Mean Std.dev T M.D 3.3571 1.53260 -8.969 -1.64286 2.7200 1.19591 -9.322 -1.82000 2.6767 .49013 -8.013 -2.06667 1.4857 .58341 -50.397 -3.51429 1.5200 .61412 -49.719 -3.54000 2.7667 1.30472 -38.760 -3.56667 2.6000 1.42849 -14.057 -2.40000 3.1800 1.69862 -13.481 -2.28000 1.3667 .49013 -9.376 -2.23333 3.1143 1.34642 -11.718 -1.88571 2.7200 1.19591 -40.069 -3.48000 3.3667 1.58622 -40.602 -3.63333 Sources of income 1.9571 1.12205 -22.689 -3.04286 2.5200 1.52850 -7.576 -1.82000 3.2667 1.57422 -5.640 -1.63333 Income earned 2.7465 1.42145 -13.359 -2.25352 3.1400 1.84070 -13.481 -2.28000 2.7000 1.41787 -6.031 -1.73333 Sources of finance 3.0429 1.33445 -12.271 -1.95714 3.1800 1.38048 -11.473 -2.48000 2.1000 1.12495 -8.885 -2.30000 Advance received 1.1857 .39168 -81.475 -3.81429 1.4600 .50346 -7.145 -1.86000 2.9333 1.41259 -14.120 -2.90000 Repayment Remuneration 2.1286 .99158 -24.228 - 2.87143 2.1000 1.03510 -9.322 -1.82000 1.4333 .50401 -15.852 -2.73333 1.4857 .58341 -21.014 -2.44286 2.5800 1.07076 -49.719 -3.54000 2.1667 1.01992 -15.852 -2.83333 Source: Primary data Significance at .01 levels Factors of NR tappers Vilavancode Thovalai 112

ISBN: - 978-93-88936-09-5 The above table shows that the age is higher among three taluks, who are in Kalkulam taluk as their mean of 3.3571 and standard deviation (1.53260) also greater than other taluks. The Mean deviation of Kalkulam taluk is -1.64, Vilavancode taluk is -1.82and Thovalai taluk is -2.06. The sexwise classification of tappers, the Mean value for Kalkulam taluk is 1.4857, Vilavancode talukis1.52 and Thovalai taluk is 2.7667 and the Standard deviation values are 0.58341, 0.61412 and 1.30472 respectively, and also to analyse the Mean deviation for three taluks are -3.51429, -3.54000, and -3.56667 respectively. The educationwise classification of tappers, the Mean value for Kalkulam taluk is 2.6, Vilavancodetalukis 3.1 and Thovalai taluk is 31.3 and the Standard deviation value are 1.428, 1.698 and 0.49 respectively, and also analyse the Mean deviation for three taluks are -2.4, -2.28, and -2.23333 respectively The experience of tappers in Kanyakumari district , the Mean value for Kalkulam taluk is 3.11, Vilavancodetalukis 2.72 and Thovalai taluk is 3.3667 and the Standard deviation value are1.346 , 1.195 and 1.58 respectively, and also analyse the Mean deviation for three taluks are - 1.88, -3.48, and -3.6333 respectively. The amount of income earned by the tappers in Kanyakumari district , the Mean value for Kalkulam taluk is 3.74, Vilavancodetalukis 3.14 and Thovalai taluk is 2.7 and the Standard deviation values are1.42 , 1.84 and 1.41 respectively, and also analyse the Mean deviation for three taluks are -2125, -2.28, and -1.7333 respectively The sources of finance of tappers in Kanyakumari district , the Mean value for Kalkulam taluk is 3.04, Vilavancodetalukis 3.18 and Thovalai taluk is 2.1 and the Standard deviation values are 1.33 , 1.3 and 1.12 respectively, and also analyse the Mean deviation for three taluks are -1.95, -2.48, and -2.4333 respectively The advance received by the tappers in Kanyakumari district , the Mean value for Kalkulam taluk is 1.18, Vilavancodetalukis 1.46 and Thovalai taluk is 2.9333 and the Standard deviation values are0.39 , 0.50 and 1.41 respectively, and also analyse the Mean deviation for three taluks are -3.88, -1.86, and -2.9 respectively The repayment of advance money by the tappers in Kanyakumari district , the Mean value for Kalkulam taluk is 2.12, Vilavancodetalukis 2.1 and Thovalai taluk is 1.4333 and the Standard deviation value are 0.99 , 1.03 and 0.50 respectively, and also analyse the Mean deviation for three taluks are -2.87, -1.82, and -2.7 333 respectively The remuneration of tappers in Kanyakumari district , the Mean value for Kalkulam taluk is 1.48, Vilavancode taluk is 2.58 and Thovalai taluk is 2.1667 and the Standard deviation value are 0.58, 1.07 and 1.01 respectively, and also analyse the Mean deviation for three taluks are -2.44, -3.54, and -2.8333 respectively 113 ISBN: - 978-93-88936-09-5 The entire co-efficient are found significant at 5 percent level. Among the independent variables, repayment, remuneration, advanced received, sources of finance and sources of income, and the dependent variables that include age, sex, education and experience are found to have positive influences. It implies that there is a close relationship between dependent and independent factors of tappers in Kanyakumari district. Paired sample test used for Natural Rubber Tapper in Kanyakumari District The following table explains that the Socio-Economic status of NR tappers in Kanyakumari district based on three taluks namely Kalkulam taluk, Vilavancode taluk, and Thovalai taluk. Paired sample test is applied and it has ten pairs. Table 4.5 Age and Sources of Finance Pair 1 Age and Sources of Finance Pair 2 Advance Received and Repayment of advance Pair 3 Education and Problems of Tapping Pair 4 Experience and Incentives received Pair 5 Income earned and Satisfaction of tappers Pair 6 Education and Training to the tappers by RB Pair 7 Sources of Income and Income earned Pair 8 Sex and Government Support Pair 9 Sources of Finance and Repayment of advance Pair 10 Age and Training to the tappers by RB The above table explains that, ten pairs of the relationship between Age and Sources of Finance applied in paired sample correlation method in the selected three taluks . In Thovalai taluk shown results as .964 higher positive correlation likewise in case of vilavancode taluk and Kalkulam taluk also shown as higher positive correlation are .907 and .934 respectively. Ho: There is no significance between Age and Sources of Finance of NR tappers in Kanyakumari district. Ho: There is no significance between Advance Received and Repayment of advance money by the NR tappers in Kanyakumari district. Ho: There is no significance between Education and Problems of Tapping by the NR tappers in Kanyakumari district Ho: There is no significance between experience and incentive received by the NR tappers in Kanyakumari district Ho: There is no significance between Income earned and Satisfaction of Tappers of NR tappers in Kanyakumari district Ho: There is no significance between Education and Training given to the tappers by Rubber Board in Kanyakumari district 114

ISBN: - 978-93-88936-09-5 Ho: There is no significance between Sources of Income and amount of Income earned by the NR tappers in Kanyakumari district Ho: There is no significance between Sex wise classification of tappers and Government Support to the NR tappers in Kanyakumari district Ho: There is no significance between Sources of Finance and Repayment of advance money by the NR tappers in Kanyakumari district Ho: There is no significance between Age wise classification and Training given to the tappers by Rubber Board in Kanyakumari district Paired sample t- test for Natural Rubber Tapper in Kanyakumari District The Paired-Samples t- Test procedure compares the means of two variables for a single group. The procedure computes the differences between values of the two variables for each case and tests whether the average differs from 0. The following table explains that the three paired sample t –test from the tappers of NR in Kanyakumari district. Table 4.6 Paired sample Pair 1 Pair 2 Pair 3 Pair 4 Pair 5 Pair 6 Pair 7 Pair 8 Pair 9 Pair 10 Paired sample t- test for Natural Rubber Tapper in Kanyakumari District Sig (2 Kalkulam taluk t-test -5.6293 -4.8972 -3.6548 4.050 -10.662 -9.918 -13.173 tailed) Status S S S S S S S .830 NS 1.885 NS -11.518 S Vilavancode taluk t-test -6.461 -6.532 -3.934 Sig (2 tailed) Status S S S 2.214 NS 2.207 NS 6.093 S -3.042 NS -12.219 11.481 S S 1.000 NS Thovalai taluk t-test Sig (2 tailed) Status -2.408 NS -5.809 S -2.536 NS 9.143 -9.049 5.385 6.595 -9.616 7.389 S S S S S S -1.795 NS Source: Primary data, Significance at .01 levels The above table stated that, since the calculated value of all the pair’s t-test is greater 0.01 levels. So the null hypothesis is accepted at 1 per cent level. It can be concluded that there is no significant differences, in Kalkulam taluk from pair 1 to pair 7 and pair 10are accepted, remaining pairs 8 and 9 are rejected. In Vilavancode taluk, from pair 1 to pair 3, pair 6, pair 8 and pair 9 are accepted, there is no significant among the pairs, and remaining pair 4, pair 5, pair 7 and pair 10 are rejected, because there is a significant among the pairs. In Thovalai taluk, from pair 4 to pair 9, pair 2 are accepted, there is no significant among the pairs, and remaining pair 1, pair 3, and pair 10 are rejected, because there is a significant among the pairs. 115 ISBN: - 978-93-88936-09-5 The Paired-Samples correlation test The Paired-Samples Correlation mean and standard Test is applied. Procedure compares the means of two variables for a single group. The procedure computes the differences between values of the two variables for each case and tests whether the average differs from 0. But the correlation value is + or – 1. The following table explains that the three paired sample correlation from the tappers of NR in Kanyakumari district. 116

ISBN: - 978-93-88936-09-5 Table 4.7 Paired sample Pair 1 Pair 2 Pair 3 Pair 4 Pair 5 Pair 6 Pair 7 P i 8 Kalkulam taluk .31429 .94286 -.68571 1.15714 .04286 .64926 .73987 .57843 .73496 .43191 The Paired-Samples correlation test applied for 10 pairs Vilavancode taluk 1.10000 4.88164 -.75714 1 58571 .54999 90878 .907 .759 .943 .838 .955 .426 .930 842 -.46000 -.64000 -.24000 .20000 .46000 .50000 1.47372 .58029 -.62000 1.44123 96065 1 66000 .934 .811 .974 .919 .603 .971 .648 802 -16667. -.73333 -.23333 1.16667 -.80000 .50000 .60000 Thovalai taluk Mean Std.dev Correlation Mean Std.dev Correlation Mean Std.dev Correlation .50346 .69282 .43142 .63888 .37905 .69149 .50401 .69893 .48423 .50855 .49827 1 76667 1 00630 .964 .794 .952 .919 .921 .963 .946 805 Pair 8 Pair 9 Pair 10 -1.58571 .90878 .75632 .28746 1.08673 .927 Source: Primary data, Significance at .01 levels .842 .894 .947 -1.66000 .96065 .66517 .28284 1.08000 .04000 .802 .887 .973 -1.76667 1.00630 .56832 .30513 .76667 -.10000 .805 .941 .972 117

ISBN: - 978-93-88936-09-5 The above table used Mean, Standard deviation and co-efficient of Correlation methods for 10 pairs of tappers in kanyakumari district. In Kalkulam taluk, all pairs shows the result of variables are more than 0.5, except the pair 6, the correlation values are 0.907 , 0.759, 0.943, 0.838, 0.955, 0.93, 0.842, 0.894, and 0.947 respectively. So it is a highly positive correlation. Among the pairs, there is the highest mean value is 1.15714 receive in the pair 4, and the highest standard deveiation value is 4.88164 received in the pair 6. In Vilavancode taluk, all pairs shows the result of variables are more than 0.5, the correlation values are 0.934 , 0.811, 0.974, 0.919, 0.603, 0.971, 0.648, 0.802, 0.887, and 0.973 respectively. So it is a highly positive correlation, except the pairs 5 and 7 are shows that results is positive correlation . In Thovalai taluk, all pairs shows the result of variables are more than 0.5, the correlation values are 0.964 , 0.794, 0.952, 0.919, 0.921, 0.963, 0.946, 0.805, 0.941, and 0.974, respectively. So it is a highly positive correlation. It signifies that in three taluks, namely Kalkulam taluk, Vilavancode taluk, and Thovalai taluk, all the paired sample correlation values reflects highly positive correlation, except pair 6 in Kalkulam taluk (0.426) since the results found is above 0.5 per cent level, so that, all these hypothesis are significant one. Sources of income earned The income from agricultural sector and labour sector of the respondents are increasing which is based on taluk wise classification in Kanyakumari district, raised in their sources of income. Total annual earnings of sample respondents’ households consist of income obtained from different sources such as tapping, animal husbandry, agriculture, drivers and casual labour. The following table explains that the sources of income earned by the tappers in three taluks in Kanyakumari district. Table 4.8 Sources Tapping Animal husbandry Agriculture Drivers Labours Total Kalkulam taluk 17 12 6 2 70 47.1 24.3 17.1 8.6 2.9 100.0 Source: Computed from Primary data Note: Figures indicate the percentage to total 118 Sources of income earned Vilavancode taluk 20 7 8 7 8 50 40.0 14.0 16.0 14.0 16.0 100.0 Thovalai taluk Respondent percentage Respondent percentage Respondent Percentage 33 9 7 5 4 5 30 30.0 23.3 16.7 13.3 16.7 100.0 Total 62 36 25 17 15 150 ISBN: - 978-93-88936-09-5 The above table inferred that the Major portion of the respondents’ income 41 per cent is obtained from rubber tapping. The contribution of labours [10 per cent] and that of agriculture is 16 per cent. The second largest source of income is animal husbandry is 25 per cent. In the Kalkulam taluk, income group share of different sources is tapping 47 per cent, agriculture 17 per cent, animal husbandry 24 per cent, casual labour 3 per cent and drivers 9 per cent. In Vilavancode taluk income group the pattern of contribution of different sources is tapping 40 per cent, agriculture and casual labours are 16 per cent, animal husbandry and drivers are 14 per cent. In the Thovalai taluk, income group share of different sources is tapping 30 per cent, agriculture and casual labours are 17 per cent, animal husbandry 23 per cent, and drivers are 13 per cent. Mode of tapping Once the trees become mature for tapping, then the demand for labour arises mainly in tapping. Majority of small holders have a tendency to hire tappers, though they are unemployed. Small growers are ready to tap themselves. The percent of small growers family members engage in tapping is very low. Educated members of the growers" families, even if unemployed, are not interested in tapping. This phenomenon naturally increases the demand for tapping labourers. The labour force consists of persons coming within the age group of 18 to 60 except students, disabled and housewives. The following table shows that the mode of tapping in Kanyakumari district. Table 4.9 Mode of tapping Mode of tapping Arasu rubber board Smallholdings Estates Own land Total Kalkulam taluk 21 28 12 9 30 40 17 13 Vilavancode taluk 18 15 11 6 70 100 Source: Computed from Primary data Note: Figures indicate the percentage to total The above table illustrated that the mode of tapping of the trees include Arasu rubber board, smallholders, estates, and own land, out of the 150 sample respondents 36 per cent of the respondents are tapping the trees in smallholdings, nearly 32 per cent of the respondents are tapping the trees in Arasu rubber board, 18 per cent of the respondents are tapping the trees in estates, and remaining 14 per cent respondents are tapping the trees in their own garden. It signifies that the majority of the respondents are tapping the trees in smallholdings, and some of them preferred to tapping the trees in Arasu rubber board, because of they are enjoyed 119 50 30.0 22.0 12.0 100 Thovalai taluk Respondent % Respondent % Respondent % 36.0 9 11 6 4 30 31.0 37.9 20.7 13.3 Total 48 54 29 19 100 150

ISBN: - 978-93-88936-09-5 some monetary and non-monetary privileges like education scholarship, medical facility, headlight, monthly salary received, housing facility etc., than the others. Payment received by the tappers Generally, tappers receive wages on the basis of the number of trees tapped or per day remuneration or monthly salary. The average wage rate for tapping 100 trees is estimated. During the period 1990-92, the rising tendency of natural rubber price caused an abnormal rise of wage by 33 percent bringing the average wage from Rs.18 per 100 trees to Rs. 24/-. In almost all the cases the nature of wage payment is piece rate. The mode of wage payment in 98 percent of cases is payment by cash. The rest of tappers ie.; 2 percent are receiving wages as half of rubber sheets they produced. Along with wage, perquisites like meals or snacks are enjoyed by negligible number of tappers. The following table explains that the remuneration received by the tappers in Kanyakumari district. Table 4.10 Mode of payment Kalkulam taluk Vilavancode taluk Thovalai taluk Payment Daily/weekly remuneration Rate per tree Monthly salary Both (A) and (B) Total Respondent % Respondent % Respondent % 26 8 31 15 16 70 11.4 44.3 21.4 22.9 100.0 13 9 16 12 50 7 18.0 32.0 24.0 100 11 9 3 30 Total 23.3 28 51 30.0 40 10.0 31 100.0 150 Source: Computed from Primary data Note: Figures indicate the percentage to total The above table stated that, the remuneration paid to the tappers include monthly basis, daily/weekly basis, rate per tree basis, and both rate per tree and non-rate per tree basis Basically, two methods of payment to the tappers, piece rate of tree and non-piece rate of tree. Out of the 150 sample respondents 34 per cent of the tappers are received payment on piece rate per tree basis, nearly 26 per cent of the tappers have received payment on daily/ monthly basis, 21 per cent of the tappers are received payment on both (A) and (B) basis, and remaining 19 per cent of the tappers are received payment on monthly salary basis. It signifies that most of the NR tappers have received payment on the basis of piece rate per tree tapping, because in Kanyakumari district NR cultivation in smallholdings. Mode of repayment by the tappers Generally, when the tappers are preferred to tapped trees in their single holdings, due to the shortage of skilled tappers are available in Kanyakumari district, the estate holders or owner of 120 ISBN: - 978-93-88936-09-5 smallholdings are given advance money to the tappers for the purpose of tapping trees in their garden permanently. The following table explains that the mode of repayment by the tappers in Kanyakumari district. Table 4.11 Mode of repayment Mode of repayment Monthly Weekly B i-Monthly Others Total Kalkulam taluk 17 23 9 8 30.0 40.0 17.1 12.9 Vilavancode taluk 10 8 6 3 36.0 30.0 22.0 12.0 Thovalai taluk Respondent Percentage Respondent Percentage Respondent Percentage 5 6 4 3 57 100.0 27 100.0 17 30.0 36.7 20.0 13.3 100.0 Source: Computed from Primary data Note: Figures indicate the percentage to total The above table stated that the mode of repayment by the tappers include monthly basis, weekly basis, Bi-monthly basis, and others. Out of the 150 sample respondents 36 per cent of the tappers are repayment of the advance money on weekly basis, nearly 32 per cent of the tappers are received payment on monthly basis, 18 per cent of the tappers have received payment on bimonthly basis, and remaining 14 per cent of the tappers are received payment on both the basis. It signifies that the majority of the NR tappers have repaid of their advance money weekly, because they are received remuneration based on piece per tree tapping, in Kanyakumari district. Table 4.12 Function of the government support Government support Excellent Satisfied Moderate Poor Very poor Total Kalkulam taluk Resp ondent Perc entage 12 17.1 13 18.6 14 20.0 20 28.6 11 15.7 Vilavancode taluk Resp ondent 9 7 9 16 9 Resp ondent 18.0 14.0 18.0 32.0 18.0 Thovalai taluk Resp Perc ondent 4 7 6 7 6 entage 13.3 25 23.3 27 20.0 29 23.3 43 20.0 26 70 100.0 50 100.0 30 100.0 150 16 18 20 28 18 100 Source: Computed from Primary data Note: Figures indicate the percentage to total The above table shows that 34 per cent of the respondents have expressed that the functioning of the government support to the tappers is satisfied. Among them 25 are in Kalkulam taluk, 16 are in Vilavancode taluk and 11 are in Thovalai taluk. Out of the total 20 per cent of the respondents felt that the functioning of the Government support is moderate. Among them 14 are in Kalkulam taluk, 9are in Vilavancodetaluk and 6 are in Thovalai taluk. And only 46 per cent of the 121 Total Perc entage Total 48 54 29 19 150

ISBN: - 978-93-88936-09-5 respondents felt that the functioning of the Government support is poor. Among them 31 are in Kalkulam taluk, 25are in Vilavancode taluk and 13 are in Thovalai taluk. Problems faced by the tappers in tapping Now-a-days the labour is the most important problem in rubber and other agricultural commodities production. The demand and supply conditions of tappers' labour market are determined by objective factors as price of natural rubber, wage rate, nature of work etc. and subjective factors such as the perspective of tapper and grower, moral ties etc. At present there is no serious scarcity of tappers, though there is scarcity of skilled persons. The trend shows that in the future the situation of scarcity of tappers, whether skilled or unskilled, will become chronic due to the nature of younger generation not to depend on tapping for livelihood, decline in rubber economy and consequent reduction in wage rate. The following table explains that the problems faced by the tappers in Kanyakumari district. Table 4.13 Problems faced by the tappers Problems in tapping Transportation Low charges Non – seasonal Natural calamities Lack of inputs Delay in payment confestration Total Kalkulam taluk Vilavancode taluk Thovalai taluk Respondent % Respondent % Respondent % 8 21 12 14 6 5 4 70 11.4 30.0 17.1 20.0 8.6 7.1 5.7 100 8 10 12 6 5 5 4 50 16.0 20.0 24.0 12.0 10.0 10.0 8.0 100.0 Source: Computed from Primary data Note: Figures in parentheses indicate the percentage to total It is clear from the above table that, the problems faced by the tappers are transportation, low wages, non-seasonal period, natural calamities, lack of inputs, delay in payment, and confestration . 13 per cent of tappers have problems like transportation, above 20 per cent of tappers have problems like low charges or low wages and non-seasonal, 10 per cent of the tappers are facing problems like lack of inputs and delay in payments and at least 6 per cent of the tappers have problem is confestration. Only few tappers are in the study area facing monkey bite, snake bite, elephant etc., In Kalkulam taluk, majority of the tappers are facing low charges or low wages is received, and only 20 per cent of the tappers are facing the problems like non-seasonal cultivation of rubber in Vilavancode and Thovalai taluks . 122 4 4 6 5 5 5 1 30 Total 13.3 20 13.3 35 23.3 30 16.7 25 16.7 16 16.7 15 9 100.0 150 ISBN: - 978-93-88936-09-5 Advance received by the tappers When the tappers are preferred to tapped trees in their single holdings, due to the shortage of skilled tappers are available in Kanyakumari district, the estate holders or owner of smallholdings are given advance money to the tappers for the purpose of tapping trees in their garden permanently. Additional nominal benefits like bonus, festival allowance, children education allowance, scholarship for SC/ST Candidates etc. are enjoyed by the tappers. The following table explains the details of those who receive advance money from their owner of the garden. Table 4.14 Advance yes no Total Kalkulam taluk 13 70 Advance received by the tappers Vilavancode taluk 81.4 18.6 100.0 27 23 50 54.0 46.0 100 Thovalai taluk Respondent Percentage Respondent Percentage Respondent Percentage 57 17 13 30 56.7 43.3 100.0 Source: Computed from Primary data Note: Figures in parentheses indicate the percentage to total The above table shows that whether the respondents getting advance money from the government or owner of the garden or not. Out of 150 respondents, 57 per cent of the respondents are getting advance money from the government or owner of the garden and remaining 43 per cent of the respondents are not getting advance money from the government or owner of the garden. Out of the 101 benefited tappers, 57 are in Kalkulam taluk, 27 are in Vilavancode taluk and 17 are in Thovalai taluk. Out of the 49 non-benefited tappers, 13 are in Kalkulam taluk, 23 are in Vilavancode taluk and 13 are in Thovalai taluk. Training programmes attended by the tappers To improve their quality and personality, Government has been conducting a number of training programmes to tappers through Rubber Board. One programme differs from the other. If they attend many such training programmes, it will sharpen their knowledge of tapping. Rubber Board make an arrangement for give training to the tappers. Tapping of rubber trees is not only a skilled job but also highly labour intensive. Decline in the natural rubber economy causes decline or stagnation in wage rate and reduction in the number of working days. Their wage rate is too low. Details regarding training programme attended by the tappers in Kanyakumari district listed in the table 4.15 123

ISBN: - 978-93-88936-09-5 Table 4.15 Opinion towards Training programmes attended Traini ng Excelle nt Satisfie d Modera te Poor Very poor Total Kalkulam taluk Vilavancode taluk Thovalai taluk Responde nt 14 15 15 16 10 70 Percenta ge 20.0 21.4 21.4 22.9 14.3 100.0 Responde nt 9 16 12 8 5 50 Percenta ge 18.0 32.0 24.0 16.0 10.0 100.0 Responde nt 5 6 11 4 4 30 Percenta ge 16.7 20.0 36.7 13.3 13.3 100.0 28 37 38 28 19 150 Total Responde nt Percenta ge 19 26 26 19 20 100 Source: Computed from Primary data Note: Figures in parentheses indicate the percentage to total Table 4.15 by using a five point questionnaire to assess the opinion regarding the Training programmes attended by tappers in Kanyakumari district. It was found that the majority of 26 per cent of the tappers have expressed satisfied and moderate of the Training programmes. 20 per cent of the tappers are feel Training programme is very poor, and remaining 19 per cent of have expressed excellent and poor of the Training programmes. Government support to the tappers Among the Government measures to uplift the NR rural tappers enjoy the Merit Award, Educational stipend, Group insurance, Medical attendance, Housing subsidy, and Housing subsidy to SC/ST tappers in the unorganised sector. The Rubber act 1947, provides amongst other functions shall be the duty of the board to secure better working conditions and the provisions and improvement of amenities and incentives for rubber tappers. The following table shows that sample respondents get supported from the Government. Table 4.16 Government support Excellent Satisfied Moderate Poor Very poor Total Kalkulam taluk 20 14 13 11 70 17.1 28.6 Government support to the tappers Vilavancode taluk 9 20.0 18.6 15.7 100.0 Source: Computed from Primary data 124 16 9 7 9 50 18.0 32.0 18.0 14.0 18.0 100.0 Thovalai taluk Respondent Percentage Respondent Respondent Respondent Percentage 12 4 7 6 7 6 30 13.3 23.3 20.0 23.3 20.0 100.0 Total Percentage 25 43 29 27 26 150 16 28 20 18 18 100 ISBN: - 978-93-88936-09-5 Note: Figures in parentheses indicate the percentage to total The Table 4.16shows that, A five point questionnaire was framed to assess the opinion regarding the Government support to the NR tappers. It was found that the majority of 28 per cent of the tappers have expressed satisfied on its Government support. 20 per cent of the tappers feel their Government support have moderate. 18 per cent of have expressed poor and very poor of the Government support. Only 16 per cent of the tappers have expressed as an excellent of the Government support to the NR tappers. Nature of assistance received from the Rubber Board The Indian Rubber Board was constituted under the Rubber (production and marketing) act, 1947. The function of the rubber board is to promote by such measures as it thinks fit the development of the rubber industry. It is also supported to the NR tappers like providing subsidy for new planting and re-planting, providing advice and extension services, Supplying technical advices, Training for rubber tapping and cultivation, Proper education and motivation, and Improving marketing of rubber. The following table explain that the Nature of assistance received from the Rubber Board by the tapper in Kanyakumari district. Table 4.17 Nature of assistance Excellent Satisfied Moderate Poor Very poor Total Kalkulam taluk 11 27 12 10 70 14.3 15.7 38.6 17.1 14.3 100.0 Nature of assistance Vilavancode taluk Respondent Percentage Respondent percentage 10 20 7 8 7 8 50 40 14 16 14 16 100 Thovalai taluk Respon dent 5 6 7 7 5 30 Percentage 16.7 20.0 23.3 23.3 16.7 Total Percentage 35 24 42 26 23 100.0 150 23 16 28 17 16 100 Source: Computed from Primary data Note: Figures in parentheses indicate the percentage to total. From the table 4.17 inferred that opinion regarding Nature of assistance received from the Rubber Board to the tappers in Kanyakumari district. Out of 150 respondents, 28 per cent of the tappers are feel as moderate , 23 per cent of the tappers are feel as an excellent, 17 per cent of the tappers are feel poor and remaining 16 per cent of the tappers are feel as satisfied and very poor in the nature of the assistance of rubber board in Kanyakumari. Year of experience Old is gold. Like that experienced tappers have more knowledge and expertise in the field of tapping than the younger generation. For the present study experienced of tappers are classified 125

ISBN: - 978-93-88936-09-5 as below 5 years, 5-10 years, 10-15 years, 15-20 years, and above 20 years. The following table presented that the experience of tappers in NR tapping. Table 4.18 Year of experience for tappers Year of experie nce below 5 years 5-10 10-15 15-20 Above 20 Total Kalkulam taluk Vilavancode taluk Thovalai taluk Respond ent 12 9 22 13 14 70 Percent age 17 13 31 18 20 100.0 Respond ent 9 13 15 9 4 50 Percent age 18 26 30 18 8 100 Respond ent 10 7 6 4 3 30 Percent age 17 23 20 13 10 100 31 29 43 26 21 150 Total Respond ent Percent age 21 19 29 17 14 100 Source: Computed from Primary dataNote: Figures in parentheses indicate the percentage to total Table 4.18 indicates that the experiences of NR tappers in Kanyakumari district, and its classified into three taluks. Out of total respondents, 43(29 per cent) are having the experience from 10-15 years, 31 (21 per cent) are having the experience of below 5 years, 29 (19 per cent) are having the experience from 5-10 years, 17(26 per cent) have the experience from 15-20 years, and remaining 21(14 per cent) have the experience of above 20 years. Amount of monthly income The income particular helps us to know the living conditions of the tappers. Income is one of the important determining factors of a marketable surplus. Rich growers don not need financial assistance from other agencies, because of their sound financial position. He does not sell out all the NR products at once. It reduces the marketable surplus. He is waiting to sell out their rubber sheets or milky rubber for higher prices. In the present study, amount of monthly income of the respondents are classified as below Rs. 20000/-,Rs. 20000/- to Rs. 25000/-, Rs. 25000/- to Rs. 30000/-, Rs. 30000/- to Rs. 35000/-, and above Rs. 35000/. The distribution of respondents according to their monthly income is depicted under the table. Table 4.19 Amount of monthly income Amou nt of income (Rs.) Below 20000 2000025000 25000Kalkulam taluk Vilavancode taluk Responde nt 17 18 15 Percenta ge 23.9 25.4 21.1 Responde nt 10 11 9 Responde nt 20.0 22.0 18.0 126 Thovalai taluk Responde nt 7 8 5 Percenta ge 23.3 26.6 16.7 Total Responde nt 34 37 29 Percenta ge 23 25 19 ISBN: - 978-93-88936-09-5 30000 3000035000 Above 35000 Total 8 12 70 11.3 16.3 100.0 8 12 50 16.0 22.0 100 5 5 30 16.7 16.7 100 21 29 150 Source: Computed from Primary data Note: Figures in parentheses indicate the percentage to total From table 4.11 it is understood that out of total respondents a maximum of 37(25 per cent) have a monthly income of Rs. 20000/- to Rs. 25000/-, followed by 34(25 per cent) have a monthly income is below Rs. 20000/--, 29 (19 per cent) have a monthly income of Rs. 25000/- to Rs. 30000/-, and above Rs.35000 and 21(14 per cent ) with a monthly income of Rs. 30000/- to Rs. 35000/-. It concluded that majority of the respondent earned monthly income of Rs 20000-25000. One way ANOVA Table 4.20 Kalkulam taluk Factors Mean square Age 16.711 232.271 Education 33.631 220.802 Sex Experience 16.711 232.271 Ho: Satisfaction of tappers with dependent variable Vilavancode taluk F -test Status Mean square S 16.711 232.271 S 33.631 220.802 S 16.711 232.271 F -test Status Thovalai taluk Mean square S 11.2 58.544 S 17.2 94.031 F -test Status S 3.903 61.273 S 3.903 61.273 S 1.519 42.734 S 16.9 92.520 S S S Source: Primary data. Significance at 5% level S – significance From the table 4.20 it is observed that the significant influencing satisfaction of tappers in kanyakumari district, include age, education, sex, and experience, among the variable , there is a high mean score of education is 33.631in Kalkulam taluk, and Vilavancode taluk, and94.031in Thovalai taluk. It inferred that an education helps to enrich the personality of the tappers in all aspects. Since the calculated value of all the F-test is greater 0.05 levels, the null hypothesis is accepted at 5 per cent level. It can be concluded that there is no significant differences, in the selected three taluks are accepted. SUMMARY This chapter deals with socio – economic profile of the tappers, tapping area, methods of tapping, Nature of assistance received from the Rubber Board, satisfaction of tappers etc. Tapper wages constitute a major component of cost of production of NR. Due to shortage of tappers and the resultant high wages, low frequency tapping systems are favoured in most of the regions. In 127 14 19 100

ISBN: - 978-93-88936-09-5 majority cases, tapping charge is calculated on per tree basis. In some places the grower himself/family member does the tapping activity. 128 ISBN: - 978-93-88936-09-5 CHAPTER - V MARKETING OF RUBBER IN KANYAKUMARI DISTRICT 5.1 INTRODUCTION The first step in the marketing of rubber is the collection of surplus goods in the case of a consumer or the whole production in the case of an industrial raw material from the individual growers. Rubber is first obtained in the form of latex. Usually it is made into sheets. The next function in the marketing of rubber is its transportation from production centre to consumption centre. Though Kanyakumari district accounts for more than 90 per cent of the rubber produced in Tamil Nadu, only less than 30 per cent is consumed in the district. The remaining quantity has to be transported to other States by ship, rail or road. Packing is another important marketing function. Rubber sheets are generally packed in rubber itself; for top quality rubber such as pale latex crepe and crepe, plywood cartons are also used; for solid block rubber polythene sheets are used. Rubber passes from the production center through different channels to the manufacturer. For the estate sector, there is no difficulty in selling the rubber when compared to the small holding sector. The estates are able to sell this rubber direct to the manufacturers. Before they dispatch their rubber, they sort, grade and pack them in standard bundles in the estates itself. The estate sector produces mostly top grade rubber and their produce is therefore in high demand. When the estates are follow a systematic way of processing and marketing. But the cases of small holdings are different. The small holders are marketed their rubber through dealers and co-operative societies. Often they do not hold any stock and storage facility. When they accumulate a week’s production, they take it to the nearest market and sell it off. Sometimes rubber is even sold to local traders who visit the holdings and collect rubber at periodical intervals. There are some dealers and merchants who advance loans given to small holders without charge interest. But the growers should market their rubber only through them. Marketing is a very wide subject involving various techniques, methods and procedures. A suitable marketing strategy is the key for successful marketing. To involve into marketing strategy, one should essentially possess update knowledge of this market. This will facilitate the building up of a picture of the market and would also provide guidelines for making a realistic sales forecast for future. For this purpose, details on the following aspects are the basic requirements; 1. Size of the market 2. Demand 3. Market structure 4. Buying habits 129

ISBN: - 978-93-88936-09-5 5. Market share 6. Overseas market The activities of rubber marketing system are connected with the movement of rubber from the primary producers to the ultimate consumers. There are distinct differences in the primary marketing system in the small holding and the estate sectors. The marketing of natural rubber in Kerala is commonly adopted by small holders and estate sectors. Small holders and estate sector sell natural rubber to traders or dealers through a marketing channel. 5.2 OPERATIONAL TERMS USED IN MARKETING OF RUBBER 5.2 (A) Retailers The rubber market is concentrated in three Taluks consisting of many villages. In each village there are nearly 10 retail shops, each retailer will purchase up to 3 tonnes per month. Retailers operated at village level. These retailers undertake the initial working of assembling the produce from the small growers in and around their place of business. Nearly 80 per cent of the small growers dispose of their products through the retailers only 50 per cent of the retailers have the licence to deal in rubber. The retailers are stationed at important villages noted for their potentials for latex production. The only factor which influences the choice of a particular retailer is his proximity of the planters. Small growers are produce 5 to 7 sheets of rubber per day. They carry the rubber sheets to the retailers either by cycle or by head-load. Those who produce 10 to 25 sheets carry the rubber by means of mopeds or cycles. But planters who produce 25 to 100 sheets of rubber use their own vehicle and jeep for transportation purpose. . 5.2 (B) Dealers The Rubber Act, 1947 regulated the activities of the rubber dealers in India. According to the Act, rubber dealers have to take out a license from the Rubber Board for dealing in rubber. The rubber dealers can be broadly divided into three:(1)Primary dealers, (2) Middle level dealers and (3) Big level dealers Unlicensed dealers are found to be prevalent to some extent in India, though it is abolished under the Rubber Act, 1947. Unlicensed dealers usually operate at interior places. They collect rubber from small holders by visiting them periodically. (a) Primary dealers Primary dealers are operated at village level. A dealer is people who are purchases rubber between 40 and 100 tonnes per month. They are called as primary dealers. b) Middle level dealers Dealers who purchase rubber between 100 and 200 tonnes per month are the middle level dealers. Sometimes middle level dealers sell directly to the industrial consumer also. 130 ISBN: - 978-93-88936-09-5 c) Big level dealers Big dealer buy and store raw rubber between 200 and 500 tonnes monthly. Some of the big dealers will receive the order from the industrial consumers and they execute it at the proper time. Some of them dispose their rubber products to the wholesalers. 5.2 (C) Wholesalers Wholesalers are industries dealing 500 to 1000 tonnes of raw rubber per month wholesales may mainly from middle level dealers, big dealers and estate holders. They in turn dispose of their rubber directly to big industrial consumers like M.R.T. ltd, Appollo tyre etc. There is no need for any broker and middleman in effecting the sale of rubber sheet to the retailer. The reason is that there is homogeneity about the prices paid for a sheet of rubber. The existing price of a kilogram of rubber sheets is Rs. 96/- the rubber sheets are off loaded at the retail shop. They are weighted and payments for the same are made without any loss of time. The retailers sell the rubber sheets mostly to the wholesaler who has established their bases of operation in Madras. While selling the rubber sheets they pay a tax of 8 per cent. They pay a tax of Rs.8, 000/- for the rubber sheets worth Rs. 1 lakh sold out to wholesaler. To avoid such tax the retailers in rubber mostly operate in black markets. To make matters worse. They manage to establish branches of their shops in busy cities as a prelude to carry on their illegal operations still more effectively. 5.2 (D) Co-operative Marketing The Co-operative Marketing Societies have entered the field of rubber marketing with the main objective of helping the small holders to secure a reasonable price for their produce. The cooperative societies dealing in rubber have to compete with private dealers who have adequate capital and a sound system of functioning to control the market. If properly organized, designed and financed these primary marketing co-operative societies can do much for solving the small holders1 marketing problems. 5.2 (E) Marketable Forms of Natural Rubber The important forms, in which the crop can be processed and marketed, are (i) Sheet rubbers (ii) Crepe rubbers (iii) Preserved field latex and latex concentrates (iv) Block rubber The crop collected in the form of latex can be processed into any of the above forms. But the crop collected as field coagulum can be processed only into crepe or block rubber. (a) Sheet rubber 131

ISBN: - 978-93-88936-09-5 Latex is coagulated in suitable containers into thin slabs of coagulum and sheeted through a set of smooth rollers followed by a grooved set and dried to obtain sheet rubber. Sheet rubbers are classified into two; Ribbed Smoked Sheets and air Derived Sheets (Pale Amber Unmasked Sheets). A major quantity of rubber in India (about 71 per cent) is marketed in the sheet form at present, as it is the oldest and the simplest method of processing latex into a marketable form. (b) Synthetic rubber Natural rubber is an all purpose rubber but due to its shortage during the Second World War period many types of synthetic rubbers were developed. They are classified as general purpose rubbers and speciality rubbers. (c) Reclaimed rubber Reclaimed rubber is manufactured by treatment of old and worn out tyres, tubes and other used rubber articles with certain chemical agents at a high temperature/pressure. Reclaimed rubber is used in the manufacture of goods usually in blend with natural or synthetic rubber. The manufacturing industry in India consumed 102,435 tonnes of reclaimed rubber during 2014-15. 5.2 (F) Brokerage and commission In Kanyakumari district as it has been stated that there are more than 200 retailers have obtained license for dealing in latex from the government through the rubber board. They are called upon to maintain proper accounts for the number of sheets produced every day from the local planters and the money paid out to them. 5.2 (G) Fluctuations in Rubber Price The price for rubber products plays a crucial role in fostering the growth of the rubber industry in Kanyakumari district. The most important rubber product are the rubber sheet, as it is well known a rubber sheet of standard size and quality weight of 1 kg with a view to find out the changes in the prices of rubber sheets day after day, month after month and year after year. The investigator has taken pains to collect details about the prices of rubber sheets from well-established producers since 1995. 5.3 (H) Inadequate Supply of Rubber The demand for rubber product is high. During 1950, India was an exporter of natural rubber and the position reversed. Thereafter day by day rubber based industries are increasing. Therefore the demand for rubber is increase. The problem faced by the rubber producers in Kanyakumari district is that they are unable to crop up with the unprecedented increase in the demand for rubber in certain environment. 5.3 (I) Price of rubber The prices of NR is currently the most significant issue of the global rubber industry and trade, as NR has now become more of a social commodity affecting the livelihood over 30 million 132 ISBN: - 978-93-88936-09-5 smallholders, worldwide. The fundamental factors influencing NR prices are demand and supply while all other factors have indirect effects. Rubber growers have been received better prices in the world, especially at the farm gate. The low prices of rubber due to fall in industrial activity, economic showdown and recession in the country and outside. 5.2 (J) Economic importance of Rubber In India the southern state of Kerala leads the production and the marketing of rubber products with a 93 percent share of total production in the whole of the country followed by Tamil Nadu, Karnataka, Tripura, Assam, and Nagaland with a combined share of 7 percent. At the global level, India enjoys the third position in the production of natural rubber next to Thailand and Indonesia which occupy the first and second slots respectively. 5.2 (K) Rubber wood In recent years rubber wood has emerged as an alternate source of timber in India. It has high environmental acceptability both in domestic and International markets. Processed rubber wood has a wide range of application like furniture, panelling, table top, flooring, household article etc. A new major source of timber a rubber wood - standard common name for the timber of Hevea brasiliensis a timber obtained from SMSs, it has high environment acceptability in the global market as a substitute for other tropical timber products. The rubber wood industry has started taking rapid strides only in the past two decades. The wood has a very uniform structure and contains large vessels that are clearly visible to the naked eye. 5.2 (L) Physical properties of Rubber Wood The wood is a moderately heavy timber with density and strength fairly close to that of oak and teak. The physical properties vis-a-vis teak are shown under the table. Table 5.1 Physical properties – dry rubber wood vs. dry teak wood Property Moisture content Specific gravity Static modulus of rapture (kg\cm2) Bending modulus of elasticity (kg\cm2) Hardness, kg side End 756.0 82.0 538.0 621 Source: Asian Rubber Handbook and Directory 2005. 5.2 (M) Consumption of Rubber Wood. Consumption of rubber wood and its products has been rising steadily since the 1980’s, particularly in the US and in the European Union, since they are recognized as eco-friendly products. The demand for rubber wood may grow very fast in the coming years for reasons already cited. The wood’s potential for replacing valuable tropical timber species and relieving the pressure 133 Rubber wood Teak wood 12.0 .557 12.0 .0604 959.0 119.6 512 488

ISBN: - 978-93-88936-09-5 off the tropical forests is substantial since the major tropical timber exporters are the main rubber producing countries. 5.2 (N) Marketing Channel Marketing channels are the alternative routes through which the products passes from the initial producer to the ultimate consumer. Therefore the marketing channel of natural rubber deals with all the intermediaries of middleman who involved in marketing of natural rubber from the initial producer to the ultimate consumer. The rubber sheet produced by the cultivators are sold either directly to the manufactures of rubber products who are the ultimate consumers or through local merchants, whole sales and growers co-operative marketing society. 5.2 (O) Branding Branding is a modern management technique to improve the domestic as well as international acceptability of a commodity. In the case of NR, it is an assurance of quality and a promise by the exporter, with the endorsement of the Board. The brand will fetch a premium price for the rubber exported which, in turn, will definitely benefit the one million primary producers of India. The brand is promoted with a view to differentiating the Indian NR on its consistent quality parameters in line with the international standards. Good brands are built on good reputation. Good reputation is built on good publicity. Production, Import, Export and Consumption of Natural Rubber: The country is saving over 2000 crores annually in foreign exchange through production of about six lakh tones of natural rubber, strategic raw material needed for industrial progress. Today India has realized the strategic importance of natural rubber as a potential foreign exchange earner. The relationship between prices of NR and its production, consumption, exports and imports to identify the nature of competition and structure of NR market in India. There is significant relationship between NR price and consumption and imports due to the influence of a few buyers on prices through their purchase and imports in an oligopolistic market. The following table shows the production, import, export and consumption of natural rubber from 2000-01 to 2013-14. Table 5.2 Production, Import, Export and Consumption of Natural Rubber In tonnes Year 2001-02 2002-03 2003-04 2004-05 Production 631400 649435 711650 749665 Import 049769 026217 044199 072835 134 Export Consumption 6995 55311 75905 46150 638210 695425 719600 755405 ISBN: - 978-93-88936-09-5 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11 2011-12 2012-13 2013-14 2014-15 Total Mean Std.dev 802625 852895 825345 864500 831400 861950 903700 913700 844000 645000 11087265 791948 97112 045285 089799 086394 077762 177130 190692 213785 217364 325190 442130 2058551 147039 121932 Source: Rubber Growers Guide, 2015 The table 5.1 clearly shows that the production of natural rubber has increased from 631400 in 2001-02 to 852895 in 2006-07, and 2007-08 onwards it may be vary till 2012-13. The import of natural rubber has varied from year to year, and the export of natural rubber also in fluctuating trend. But there is an abnormal decline of export is 5398 tonnes in the year 2013-14. The consumption of natural rubber has increased from 638210 in 2001-02 to 1020910 in 2014-15. The above table also shows that output of the Mean and Standard Deviation. It is noted from the table that the mean value for the production of rubber is791948 tonnes , and consumption of rubber is 855790 tonnes, imports of rubber is 147039 tonnes, and exports of rubber is 38650 tonnes and the standard deviation value are 97112, 120512, 121932 and 24499 tonnes respectively. From the study we understood, that consumption exceeds production of NR in India, gap might be fulfilled by the imports. 5.3 INDIAN RUBBER MARKET India’s production of NR varies between 6 and 7 lakh tons annually which amounts to Rs. 3000 crores. 70 percent of the total rubber production in India is in the form of Ribbed Smoked Sheets (RSS). This is also imported by India accounting for 45 percentage of the total import of rubber. The Indian rubber industry has a turnover of Rs. 12,000 crores. Most of the rubber production is consumed by the tyre industry which is almost 52 percentage of the total production of India. Among the states, Kerala is the leading consumer of rubber followed by Punjab and Maharashtra. 5.4CHARACTERISTICS OF NATURAL RUBBER MARKET IN INDIA In India, NR is produced in southern states especially in Kerala but it is consumed all over the country. It necessitates the connecting link between producers and consumers through rubber 135 73830 56545 60353 46926 025090 029851 027145 030594 5398 1002 541095 38650 24499 801110 820305 861455 871720 930565 947715 964415 972705 981520 1020910 11981060 855790 120512

ISBN: - 978-93-88936-09-5 dealers and agents. The presence and influence of a few big dealers in the Indian rubber market cannot be overlooked. They are able to control the supply side of NR by adopting their profitmaking strategies. a) Indian rubber market has been influenced by the pressure tactics of two different pressure groups of producers and manufacturers to protect their own interests. b) The Indian rubber market has no mechanism to dispose of the accumulated stock of rubber especially during peak production periods. But when the market feels scarcity of rubber, it will be rectified through import within no time. 5.5 PROBLEMS FACED BY SMALL GROWERS IN MARKETING The small growers are occupying a very dominant position in area and in production in the rubber plantation industry in India, their problems related to marketing ' are complex. 1. Difficulties in Regular Disposal of Stock Timely and regular disposal of rubber at reasonable prices is very important to the small holders. But accumulation of very high stocks with growers is a regular feature in the peak production period in September - January. Generally, the stocks are left at normal level only from the month of April and the small holders are affected by this. 2. Difficulties in Grading Visual grading invariably leads to the unhealthy trading practice of downgrading because the system is subjective in nature. Down grading exists during periods of slackening demand when the buyer is less anxious to buy and the seller is more anxious to sell. In such situation the price and grade will be fixed at the buyer's choice. "No grading agency at present functions to confirm or contradict grading which is usually done by the buyer." The small holders whose holdings capacity is poor are in the midst of this unhealthy practice. 3. Lack of Certainty in the Market This is also a problem incidental to accumulation of stock with growers and lack of demand. Wide fluctuations in the market prices of rubber market are based on the demand and supply of rubber. Under such conditions there is no guarantee that the small holders are assured of at least the minimum notified prices. The uncertainty in the market leads to the exploitation of the small holders by the dealers and middlemen. 4. Price fluctuations The Rubber Act vests the Central Government with powers for prohibiting, restricting and controlling import and exports of rubber either in general or in specified classes. The Act empowers the Government to notify prices. Minimum and maximum prices are fixed and notified so that growers may know what minimum prices they are entitled to and the rubber users may know 136 ISBN: - 978-93-88936-09-5 what prices have to be paid by them. The Indian rubber market is characterized by wide fluctuations in the market prices. 5. Exploitation by Middleman Another problem encounter in marketing of rubber in Kanyakumari district is the exploitation by middleman. The number of intermediaries and middlemen between the farmer and final consumer is too many and the margin is large. The middle men are playing a dominant role in the marketing of rubber in Kanyakumari district. They get a huge of money from the sellers and the buyers. 6. Inadequate Supply of Rubber The demand for rubber product is high. During 1950, India was an exporter of natural rubber and the position reversed. Thereafter day by day rubber based industries are increasing. Therefore the demand for rubber is increase. The problem faced by the rubber producers in Kanyakumari district is that they are unable to crop up with the unprecedented increase in the demand for rubber in certain environment. Foreign Trade of Rubber Products in India Balance of trade is an important factor for the determination of national economy. There is the difference between export and import is called as balance of trade. The following table explains that the Foreign Trade of Rubber Products in India, from 2001-02 to 2014-15. To ascertain the significant difference between export and import, “T” statistics and correlation are administered. The resultant mean and standard deviation are also presented in the table. Table 5.3 Foreign Trade of Rubber Products in India Year 2001-02 2002-03 2003-04 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11 2011-12 2012-13 2013-14 2014-15 Total Mean Exports (MT) 6995 55311 75905 46150 73830 56545 60353 46926 25090 29851 27145 30591 5398 1002 541095 38650 Imports (MT) 49769 26217 44199 72835 45285 89799 86394 77762 177130 190672 213785 262753 360263 442130 2058551 147039 137 Balance of Trade (MT) -42774 +29094 +31706 -26685 +28545 -33254-26041 -30836 -152040 160841 -186640 -232159 -354865 -441128 -1242982 -95614

ISBN: - 978-93-88936-09-5 Std.dev correlation 24499 -0.745 121932 -193861 Source: Rubber Asia, May – June 2015, As it is evident from above table 5.3 that, exports of rubber product from India have been on increased, from 6995 tonnes in 2001-02 to 60353 tonnes in 2007-08 except 2004-05 and 2006-07 were slightly decreased export of rubber sheets. During the study period, from 2001-02 to 2014-15 all the years except 2002-03, 2003-04, 2005-06, 2013-14 and 2014-15, the balance of trade is negative, because of imports exceeds exports. This study also reveals that balance of trade is deficit is -1242982 tonnes of NR sheets. Imports and exports of NR are in fluctuating trend. But, 2009-10 onwards import shows an increasing trend. It signifies that, imports exceeds export, the national economy might be affected. So the government should motivate the production of NR growers through Rubber Board. This is mainly due to the economic policies of our country such as globalization, liberalization and privatization. The above table shows that output of the Mean and Standard Deviation. It is noted from the table that the mean value for the export is 38649, import is 152785.2 and balance of trade deficit is - 95614 tonnes and the standard deviation value are, 24499 and 128597 respectively. Thus, it is concluded that there is no significant relation between import and export. Since the correlation value is -0.745, is a negative correlation.. Natural Rubber Prices After the opening up of the Indian economy in 1990s, India’s domestic rubber market started showing links to the international market. Probable determinants of volatility in natural rubber prices in domestic market are state-administered procurement programs, inconsistent import and export policies and global market trend. Global market natural rubber price rose in anticipation of future supply tightness and shortage of some grades, because Australia forecasted to decline NR production due to serious drought. The prices have reached to Rs.18, 807per quintal as on June, 2012. Demand for Rubber is most evenly distributed over the years but due to absence of production in monsoon, supply shrinkage occurs. By June / July the production becomes normal, but lingering rains last for a couple of months. During rains tapping of rubber trees disrupted and production falls. Resultantly prices would rise at that time, unless there is an acute economic depression or similar negative factors. The following table explains that the Annual average rubber price (Rs. 100 kg) in domestic market from 2001-02 to 2014-15. Table 5.4 Annual Average Rubber Price (Rs. 100 kg) in Domestic Market from 2001-02 to 2014-15. RSS 1 RSS 2 RSS 3 RSS 4 RSS 5 EBC 2X Latex ISNR 20 2001-02 3472 3369 3247 3109 2961 2643 138 4105 2756 Year ISBN: - 978-93-88936-09-5 2002-03 3958 3859 3761 3621 3402 3122 2003-04 5170 5072 4972 4814 4649 4576 2004-05 5942 5842 5742 5571 5401 5272 2005-06 6404 6303 6203 6068 5865 5717 2006-07 9324 9191 9089 8783 8551 8338 2007-08 9509 9390 9290 9006 8772 8514 5039 3339 6202 4670 7064 5310 7163 5808 10868 8454 10460 8654 2008-09 11146 11046 10946 10775 10547 10076 11977 10387 2009-10 10318 10183 10081 9756 9473 8889 11756 9080 2010-11 15015 17352 17245 16908 16405 15570 11207 15865 2011-12 23096 22667 22201 21668 21238 20478 13349 20967 2012-13 19399 19053 18724 18439 18002 17484 12174 17625 2013-14 18522 17865 17162 16880 16355 15485 11956 15897 2014-15 15228 15045 14523 14115 13493 10218 8237 12287 Total 167682 167397 164128 160192 155479 146124 140954 151177 Source : Rubber Board, 2015. The table 5.4 exhibits that the Annual average rubber prices (Rs. 100 kg) in domestic market from 2001-02 to 2014-15. Price of latex increased with the increase in the price of RSS grades. Latex price increased by more than 200 percent in the last ten years. In 2001-02, price of latex per 100 kg was Rs. 4105, whereas by 2011-12 the price reaches to Rs. 13349 per 100 kg. By 2014-15 the price of latex declined to Rs. 8237 per 100 kg as shown in the above table. Same trend is visible in the case of ECB2x and ISNR 20 where the price reached to record levels by 2011-12 where the price stood at Rs. 20478 per 100 kg in the case of ECB2x and Rs. 20967 per 100 kg of ISNR20. Later, similar to RSS grade rubber price, the price of ECB2x and ISNR 20 declined almost by 40 percent by the end of 2014-15. .5.6 INTERNATIONAL RUBBER SCENARIO The first International Natural Rubber Agreement was signed in 1979 under the auspices of the United Nations Conference on Trade and Development (UNCTAD) and then renegotiated in the mid-1980s (1987 Agreement) and during 1994-1995 (1995 Agreement). The Agreement was managed by the International Natural Rubber organization (INRO), headquartered in Kuala Lumpur, Malaysia. The 1979 Agreement had as member’s seven exporting countries accounting for about 95 per cent of world exports and 25 importing countries. The objective of the 1979 agreement was to reduce excessive price fluctuations around the trend in rubber market prices. Exporting countries also stressed the need to support prices at levels considered remunerative to producers and to help to stabilise export earnings and increase these earnings based on expanded export volumes. Basic differences between importing and exporting countries for NR led to wide divergences in negotiations, particularly as regards price level. 5.7 IMPORT OF NATURAL RUBBER: India had been an importer of NR as result of the faster growth of the rubber goods manufacturing industries in the country. NR can be imported to India free of licence from 1 April 139

ISBN: - 978-93-88936-09-5 2001. As per provisions of GATT 1994, each WTO member country is bound to limit its import duty ceiling, called bound rate, which was fixed by member’s country. The bound rate committed by India is 25 per cent for all forms of NR except for NR latex. In India had been an importer of NR as result of the faster growth of the rubber goods manufacturing industries in the country. The consumption has been growing at a faster rate than production during the last few decades as a result of captive domestic market supplemented by the protectionist policies pursued by the government. Import of NR in India during the period from 2001 to 2005 was permitted only through the customs port of Kolkata and Visakhapatnam. EXPORT OF NATURAL RUBBER: Export of NR during the crucial period has not only helped to reduce the excess stock of NR in the country, but also has been instrumental in maintaining the demand-supply balance in spite of the surge in imports. The main factors contributing to the buoyancy in export were the export promotional measures adopted by the government of India and the prevalence of relatively higher NR price in the international market since June 2003. The following table explains the Month wise export of NR from our country from 2002-03 to 2012-13. 140 ISBN: - 978-93-88936-09-5 Table 5.5 Month wise export of Natural Rubber Million tonnes Month 2002-03 2003-04 2004-05 2005-06 2006-07 2007-08 2008-09 APRIL JUNE 3928 MAY 2697 2917 JULY 3738 AUGUST 1200 SEPTEMBER 6159 OCTOBER 6814 DECEMBER 4740 JANUARY 4264 1180 1244 1898 3046 541 7197 1278 2013 1714 897 972 301 476 732 3590 5989 6722 9901 8497 6311 10226 6451 10028 6386 NOVEMBER 5872 10814 9879 12440 6346 6337 7317 9613 4670 992 FEBRUARY 4676 10410 1735 5850 MARCH 8306 13740 2358 8447 Std.Dev 4609 1954 5915 4372 3845 6152 4076 4136 8232 10534 11372 1981 973 941 656 721 3552 4712 3999 2164 4141 4105 4406 1416 1166 811 1246 5126 8053 14643 13076 4093 3999 3690 3159 9451 9299 3047 2314 200910 726 124 46 30 91 574 201011 864 149 55 36 109 682 201112 642 408 141 26 80 496 2012-13 708 460 159 39 98 559 3040 2274 2704 4969 5600 4101 4165 4952 5605 6317 3131 2563 3048 2219 2500 1475 2906 3455 2515 2834 2257 5556 6618 4818 5430 1962 6035 7178 5226 5890 TOTAL 55311 75905 46150 73830 56545 60353 46926 25090 29850 27145 30594 Mean 3810 3751 2487 2262 2549 4093 3810 864 642 Source : Secondary data. Rubber Growers Companion – 2015, NMCE 2014 708 141

ISBN: - 978-93-88936-09-5 The table 5.5shows that the month wise export of NR from 2002-03 to 2012-13, during the study period total value of exports are fluctuating. Export of Indian NR, which was little known in the overseas market until 2001-02, managed to get a break in the highly competitive global NR market. Export of NR from India rose from the low level of 6995 tonnes in 2001-02 to 75905 tonnes in 2003-04. In 2005-06 exports regained their lost ground rising to 73830 MT and 56545 MT of NR were exported during 2006-07, down from the previous year as the Indian prices were high compared to the prices in international market. The above table also shows that the output of Mean and Standard Deviation. It is noted from the table that the mean value for from 2002-03 to 2006-07 are 4609, 5915, 3845, 6152, and 4712 and the standard deviation values are 1954, 4372, 4076.7, 4136.8 and 3999 respectively. It signifies that there is a major change in the NR trade. During 2005 VAT has been introduced in Kerala, and that the state accounts for 92 per cent of the production. VAT had an impact in both exports as well as domestic trade. Export promotion measures such as identifying and encouraging potential exporters and overseas buyers, promoting buyers-sellers interaction through website and participation in international trade fairs are continuing. The main factors contributing to the buoyancy in export were the export promotional measures adopted by the Government of India and the prevalence of relatively higher NR prices in the international market since June 2003. Country wise Export of Natural Rubber From the ports, rubber was in the hands of mainly Brazilian, British and American exporters. Contrary to what Weinstein (1983) argued, Brazilian producers or local merchants from the interior could choose whether to send the rubber on consignment to a New York commission house, rather than selling it to a exporter in the Amazon (Shelley, 1918). Rubber was taken, like other commodities, to ports in Europe and the US to be distributed to the industries that bought large amounts of the product in the London or New York commodities exchanges. A large part of rubber produced was traded at these exchanges, but tire manufacturers and other large consumers also made direct purchases from the distributors in the country of origin. The following table explains that the Countrywise Export of Natural Rubber from 2010-11 to 2013-14. Table 5.6 Country wise Export of Natural Rubber ‘000 tonnes Country Thailand Indonesia Vietnam China 2010-11 2598 3130 950 84 2011-12 1406 4150 1613 29 142 2012-13 3121 2531 1023 14 2013-14 3664 2719 1076 13 ISBN: - 978-93-88936-09-5 India Malaysia Sri lanka Philippines Cambodia World Total Mean Std.Dev 20 1555 66 45 120 10233 18801 1880 3102 23 1273 26 40 100 9876 18536 1853 2833 15 1344 50 38 60 8673 18555 1856 3040 25 1380 36 32 86 9743 20651 2065 3004 Source: rubber growers guide 2015 The table 5.6 examine that country wise export information of Natural Rubber during the study period from 2001-02 to 2013-14 , the leading export market of Indian Natural rubber are China, Malaysia, Indonesia, Malaysia, Turkey, Sri Lanka, Spain, Pakistan, Singapore and Nepal. In 2011-12, export of NR decreased to 27,145 MT as compared to 29,851 MT in 2010-11. Sri lanka’s share in the total export increased to 24.4 per cent with 6,623 MT as compared to 2,619 last year. Malaysia continued to remain the second largest export destination for India at 4,273 MT and 15.7 per cent share as compared to 6,555 MT and 22 per cent share. The above table also shows that the output of Mean and Standard Deviation. It is noted from the table that the mean value for country wise export of NR from 2010-10 to 2013-14 are 1880, 1853, 1856, and 2065, and the standard deviation values are 3102, 2833, 3040 and 3004 respectively. SOCIO ECONOMIC STATUS OF NATURAL RUBBER DISTRIBUTORS IN KANYAKUMARI DISTRICT Distribution of sample respondent by channel of distribution A channel of distribution is an organized net-work or a system of agencies and institutions which, in combinations, perform all the activities required to link producers with users and users with producers to accomplish the marketing task. Marketing channels are routes through which agricultural products move from producers to consumers. The length of the channel varies from commodity to commodity, depending on the quantity to be moved and the form of consumer demand and degree of regional specialization in production. The following table explains the channel of distribution of rubber Table 5.7 Channel of distribution Categories Taluk Kalkulam Vilavancode Village traders 37 (52.9) 26 Co-op society 18 (25.7) 12 143 Commission agent 10 (14.3) 9 Wholesalers 5 (7.1) 3 Total 70 (100) 50

ISBN: - 978-93-88936-09-5 Thovalai Total (52) 16 (53.3) 79 (52.6) (24) 7 (23.3) 37 (24.6) (18) 4 (13.3) 23 (15.3) Source: Computed from Primary data Note: Figures in parentheses indicate the percentage to total The table 5.7 explain that, Out of the 150 respondents, 53 per cent are making their distribution through village traders. Other 25 per cent respondents make their distribution through co-operative society, 15 per cent respondents make their distribution through commission agents and rest of 7 per cent respondents make their distribution through wholesaler. It clearly reveals that majority of the respondents make their distribution through village traders because of respondent received input materials, advance loan from the village traders. Distribution of sample respondent by sources of finance Finance plays a pivotal role in all walks of life. The rubber producers need poor adequate money for the production and marketing of rubber. The financing sources of respondents are given under the table. Table 5.8 Sources of finance Sources Taluk Kalkulam Vilavancode Thovalai Total Bank Agent 25 17 (24.3) 11 (22) 3 (10.0) 31 (20.6) (35.7) 18 (36) 13 (43.3) 56 (37.3) Private Others 19 9 (27.1) 12 (24) 12 (40.0) 43 (28.6) (12.9) 9 (18) 2 (6.7) 20 (13.3) Total 70 (100) 50 (100) 30 (100) 150 (100) Source: Computed from Primary data Note: Figures in parentheses indicate the percentage to total The above table shows that 37 per cent of the respondents use agent funds, 28 percent of the respondents use private funds, 21 per cent of the respondents use bank funds and least 13 per cent of the respondents use other funds like SHGs. Most of the respondents are using non-banking funds, they are feels that charges high rate of interest and plantation property as a security. Among the Kalkulam taluk, 36 per cent of the respondents use agent funds, 27 percent of the respondents use private finance, 24 per cent of the respondents use bank finance and least 13 per cent of the respondents use other funds like SHGs. 144 (6) 3 (10.0) 11 (7.2) (100) 30 (100) 150 (100) ISBN: - 978-93-88936-09-5 Among the Vilavancode taluk, 36 per cent of the respondents use agent funds, 24 percent of the respondents use private finance, 22 per cent of the respondents use bank finance and least 18 per cent of the respondents use other finance like SHGs. Among the Thovalai taluk, 43 per cent of the respondents use agent finance, 40 percent of the respondents use private finance, 10 per cent of the respondents use bank finance and least 7 per cent of the respondents use other finance like SHGs. Distribution of Sample Respondent facing Problems in Storage Storage is an important function of marketing. It is the process of holding preserving goods from the time they are produced until they are needed for consumption or use. Storage protects commodities from deterioration. Moreover, surplus is carried over for future consumption during the period of scarcity. The following table explains the problems relating to storage of rubber sheets. Table 5.9 Taluk Loss of Kalkulam Vilavancode Thovalai Total weight 8 (11.4) 7 (14) 6 (20.0) 21 ( 14) Lack of storage facility 10 (14.3) 8 (16) 5 (16.7) 23 (15.3) Problems in Storage Problems Deterioration in quality 8 (11.4) 7 (14) 2 (6.7) 17 (11.3) Package 15 (21.4) 11 (22) 7 (23.3) 33 (21.9) Labour Charges 20 (28.6) 12 (24) 8 (26.7) 40 (26.6) Grading 9 (12.9) 5 (10) 2 (6.7) 16 (10.6) 70 (100) 50 (100) 30 (100) 150 (100) Source: Computed from Primary data. Note: Figures in parentheses indicate the percentage to total The above table inferred that, the respondents face six problems for storage of rubber sheets viz loss of weight, lack of storage facility, and deterioration in quality, package, labour charges, and grading. Most of the respondents are feels that labour charges are high. Out of 150 respondents, 40 opined that the labour charges is high, 33 face package, 23 face lack of storage facility, 21 face loss of weight, 17 face deterioration in quality, and 16 face grading problems. In Kalkulam taluk, has 70 respondents ,20 opined that the labour charges is high, 15 face package, 10 face lack of storage facility, 9 face grading, 8 face loss of weight and deterioration in quality problems. In Vilavancode taluk, has 50 respondents ,12 opined that the labour charges is 145 Total

ISBN: - 978-93-88936-09-5 high, 11 face package, 8 face lack of storage facility, 7 face loss of weight and deterioration in quality, and least 5 face grading problems. In Thovalai taluk, has 30 respondents ,8 have opined that the labour charges is high,7are face package,6 face loss of weight,5 lack of storage facility, 2 respondents faced deterioration in quality and grading problems Distribution of Sample Respondent Facing Problems in Marketing The activities of rubber marketing system connected with the movement of rubber from the primary producers to the ultimate consumers. There are distinct differences in the primary marketing system in the small holding and the estate sectors. Marketing of rubber is noted for its unique characteristic features. It is basically different from the marketing of any other agricultural rubber product. The marketing of natural rubber in Kerala is commonly adopted by small holders and estate sectors. Small holders and estate sector sell natural rubber to traders or dealers through a marketing channel. The following table explains the problems in marketing. Table 5.10 Problems in Marketing Problems Taluk Kalkulam Vilavancode Thovalai Total Transport tation 8 (11.4) 6 (12) 3 (10) 17 (11.3) Labour charges 12 (17.1) 10 (20) 5 (16.7) 27 (17.9) Package 5 (7.1) 4 (8) 3 (10) 12 (7.9) Comp etion 15 (21.4) 10 (20) 8 (26.7) 33 (21.9) Comm/ Broker 10 (14.3) 9 (18) 4 (13.3) 23 (15.3) Storage/ taxes 16 (22.9) 9 (21) 6 (20) 31 (20.6) license Total 4 70 (5.7) 2 (4) 1 (3.3) 11 (7.3) (100) 50 (100) 30 (100) 150 (100) Source: Computed from Primary data. Note: Figures in parentheses indicate the percentage to total From the table 5.10, it is known that the rubber distributors are facing numerous problems. The basic problems include transportation, labour charges, package, competition, commission/brokerage, storage/taxes, and license. Above 20 per cent opinion that the competition and storage /taxes problems, 18 per cent of the respondents feel that the labour charges are high, 15 per cent respondents feel that the commission/brokerage problem, 11 per cent respondents are feel that the transportation charges, and remaining 7per cent and 7 per cent respondents are feel that the package and license problems respectively. Distribution of Sample Respondent by Sales through Wholesalers Whole sales are industries handling 500 to 1000 tonnes of raw rubber per month wholesales may mainly from middle level dealers, big dealers and estate holders. They in turn dispose of their 146 ISBN: - 978-93-88936-09-5 rubber directly to big industrial consumer like M.R.T. ltd, Appollo tyre etc., the following table explain the reasons for rubber sheets are sold through the wholesalers. Table 5.11 Reasons for Rubber Sheets sold Through Commission agents Reasons Taluk Kalkulam Vilavancode Thovalai Total Credit facility 19 (27.1) 9 (18) 5 (16.7) 33 (21.9) Long term practice 11 (15.7) 8 (16) 4 (13.3) 23 (15.3) Prompt payment 9 (12.9) 9 (18) 5 (16.7) 23 (15.3) Low risk 13 (18.6) 9 (18) 3 (10.0) 25 (16.6) Better prices 4 (5.7) 5 (10) 7 (23.3) 16 (10.6) Provide inputs 14 (20.0) 10 (20) 6 (20.0) 30 (20.0) Total 70 (100) 50 (100) 30 (100) 150 (100) Source : Computed from Primary data. Note: Figures in parentheses indicate the percentage to total. The above table shows that the Sale of Rubber Sheets through Commission Agents. The reasons are credit facility, prompt payment, long term practice, low risk, better prices and provide inputs. Majority of the respondents wish to sell their rubber sheets for getting advance money (credit facility) from the commission agents. Out of 150 respondents 22 per cent of the respondents have preferred for credit facility, 20 per cent respondents preferred for provide inputs, 17 per cent respondents have preferred for low risk, 15 per cent respondents preferred for long term practice and prompt payment and least 11 per cent respondents preferred for better prices. PROBLEMS IN TRANSPORTATION (MARKETING) Transportation is the physical means, to move the goods and people from one place to another place. It is an essential component in the wheel of marketing. Natural Rubber cultivators had to face some problems in transportation of cultivation. The problems related to Non-available of vehicle, Quality adulterations, more hire charges, improper material handling, Poor conditions of road, Taxability, and Loading /Unloading. The cultivators are asked to rank the seven problems according to their preferences. Table 5.12 Problems in Transportation (Marketing) : Factors Non-available of vehicle Quality adulterations More hire charges Improper material handling Poor conditions of road 147 Total Mean score 1140 3.8 1040 975 980 940 3.47 3.25 3.27 3.13 Rank I III V IV VI

ISBN: - 978-93-88936-09-5 Taxability Loading /Unloading 845 1140 2.82 3.80 VII I Source: survey data: From the above table shows that, ranking the problems of transportation in marketing of rubber sheets. It is clear that loading/unloading and Non-available of vehicle were the main problem faced by the cultivators, which secured 38 per cent mean score. Then, quality adulterations was the third most important problem and secured 34 per cent mean score, Improper material handling was the fourth problem and secured 33 per cent mean score, More hire charges was the fifth problem and secured 32 per cent mean score, Poor conditions of road was the sixth problem and secured 31 per cent mean score, taxability has secured 28 per cent was the least problem faced by the cultivators. Problems in marketing cost for Rubber sheet distribution The Rubber Sheets are produced by the cultivators and sold either directly to the manufactures (ultimate consumers) of rubber products or through local merchants, whole sales and co-operative marketing society. Basically, the following problems in marketing cost for distribution of Rubber Sheets like transportation, Storage, Loading/Unloading, Commission charges, Incidental charges, Taxability, Abnormal loss and Cheated by middleman. Table 5.13 Cost of Marketing Per month (in Rs.) Factors Transportation Storage Loading/Unloading Commission charges Incidental charges Taxability Abnormal loss Cheated by middlemen Total Mean score 1135 970 1070 1030 990 940 935 1120 3.78 3.23 3.56 3.43 3.3 3.13 3.11 3.73 Rank I VI III IV V VII VIII II Source: survey data: The above table shows that, ranking the problems in marketing cost for Rubber sheet distribution. It is clear that first rank was received for transportation (M=3.78) per cent, followed by second rank towards cheated by middlemen (M=3.73) , third rank towards loading/unloading (M=3.56), fourth rank towards commission charges (M=3.43), fifth rank towards incidental charges (M=3.43), sixth rank towards storage (M=3.23), seventh rank towards taxability (M=3.13), and finally the eighth ranks for abnormal loss (M=3.11). Reasons for sales through commission agents A cultivator, who has to borrow heavily for a growing crop, often mortgages it in advances. So that, the sale of produce, which is hardly more than a mere formality takes, almost in his fields 148 ISBN: - 978-93-88936-09-5 as soon as the rubber is conversion into sheets. In all cases where sheet rubber is not formally mortgaged it has to be disposed of almost immediately after cultivation in order to pay off the debt. The following table explains the Reasons for sales through commission agents. Table 5.14 Reasons Credit facility Long term practice Prompt payment Low risk Better prices Provide input No storage charges Reasons for Rubber sheets sales through commission agents: Total 1138 1126 1022 1076 744 695 786 Mean score 3.79 3.75 3.40 3.58 2.48 2.31 2.62 Rank I II IV III VI VII V Source: survey data: From the above table shows that, ranking the Reasons for sales through commission agents. It is clear that first rank was received for credit facility (M=3.79) per cent, followed by second rank towards long term practice (M=3.75), third rank towards low risk (M=3.58), fourth rank towards prompt payment (M=3.40), fifth rank towards no storage charges (M=2.62), sixth rank towards better prices (M=2.48), and finally the seventh rank towards provide input. (M=2.31). Factors for getting information about price of rubber sheets To fix a price for a product this is based on quality, brand, size, colour and package of the product. The fundamental factors influencing NR prices are demand and supply while all other factors have indirect effects. Rubber growers have been received better prices in the world, especially at the farm gate. Low prices of tuber due to fall in industrial activity, economic showdown and recession in the country and outside. The following table explain that the Factors for getting information about price of rubber sheets. Table 5.15 Factors for getting information about price of rubber sheets. Factors Radio/TV Friends & relatives Local traders Commercial agents 380 260 180 104 27 250 180 165 120 90 Newspapers Website Others 200 200 180 140 80 H.S S.M M V.P Poor T.score Mean Rank 475 280 195 60 40 410 240 168 92 56 580 256 138 80 34 550 280 180 70 25 1050 966 1088 1105 951 805 800 210 193 218 221 III IV II I 191 V 161 VI 160 VII Source: survey data From the above table shows that, ranking the Factors for getting information about price of rubber sheets. It is clear that, most of the respondents were receive information from newspaper, 149

ISBN: - 978-93-88936-09-5 which secured 1105 total score, local traders rank second, Radio/TV rank third, friends and relatives rank fourth, commercial agents rank fifth, website rank sixth and others rank seventh. Others include rubber board, mobile etc., It signifies that the majority of the respondents are educated; they can easily accessing the price movement of rubber sheets in the domestic market as well as international market. Maintenance cost of rubber sheet Cost means all expenses for cultivation of rubber. It includes operation cost, land tax, cost of vessels, sheet drying, fuel expenses, electricity charges etc., and the following table explains that the Maintenance cost of rubber sheets. Table 5.16 Cost Operation cost Land Tax Cost of vessels Sheet drying Fuel expenses Electricity charges Maintenance cost of rubber sheet Total 1021 806 958 1029 992 841 Mean score 3.40 2.68 3.19 3.43 3.30 2.80 Rank II VI IV I III V Source: survey data: From the above table shows that, ranking the Maintenance cost of rubber sheets. It is clear that first rank was received for sheet drying (M=3.43) per cent, followed by second rank towards operation cost (M=3.40), third rank towards fuel expenses (M=3.30), fourth rank towards cost of vessels (M=3.19), fifth rank towards electricity charges (M=2.80), and finally the sixth rank towards land tax. (M=2.68). Table 5.17 Reasons No storage cost No Transportation cost Credit facility Easy sale Prompt payment Reasons for rubber sheets sales through wholesales Total Mean score No Commission charges Better prices 1048 824 1097 1135 1010 918 932 3.49 2.74 3.65 3.78 3.36 3.06 3.10 Rank III VII II I IV VI V Source: survey data: From the above table shows that, ranking the Reasons for sales through wholesales. It is clear that first rank was received for No Transportation cost (M=3.78), followed by second rank towards No storage cost (M=3.65), third rank towards No commission charges(M=3.49), fourth 150 ISBN: - 978-93-88936-09-5 rank towards credit facility (M=3.36), fifth rank towards prompt payment (M=3.10), sixth rank towards easy sale (M=3.06), and finally the seventh rank towards better prices (M=2.74). Table 5.18 Problems faced by the distributors of rubber sheets Problems Storage Market Kalkulam taluk Vilavancode taluk Thovalai taluk Chisquare 10.057 11.143 Sheet purchase 21.200 Transportation 20.600 P value .074 .133 .033 .002 Chisquare .133 .079 .043 P value Chi4.240 13.000 6.400 11.120 11.320 13.000 8.933 12.933 square 8.400 P value .269 .299 .257 .044 Ho: There is no significant relationship between distributors and the problems in marketing of rubber sheets in Kanyakumari district. H1: There is significant relationship between distributors and the problems in marketing of rubber sheets in Kanyakumari district. The above table shows output of the Chi-square value, degrees of freedom, and significant value. In order to analyse the significant relationship between distributors and the problems in marketing of rubber sheets in Kanyakumari district. There are four problems such as Storage, Market, Purchase of sheets, and Transportation have been identified and tested with the tool of Chisquare. It is also observed from the table that there is significant relationship between distributors and the problems faced by the distributors in marketing of rubber sheets in Kanyakumari district, since the P value are for transportation in three taluks like Kalkulam, Vilavancode and Thovalai .002, .043 and .044 respectively, thus it is concluded that there is a significant relationship between the distributors and the problems in marketing of rubber sheets in Kanyakumari district. It is found that the P value is lesser than .05, thus the alternative hypothesis H1 is accepted, and other problems like storage, sheets purchase and market, also shows that the P value is greater than .05 per cent , then the hypothesis is rejected but the null hypothesis is accepted. Socio economic status of NR distributors in Kanyakumari district (One sample test) at 5% level of significance There are so many factors determine the socio economic status of NR growers and distributors such as Type of Distributors, Age, Education, Marital Status, Income, Family Size, Nature of Family, Time Spent, Types of Subsidy, Area of Market and Sources of Finance. To ascertain the significant difference between the NR distributors based on three taluks in Kanyakumari district and their socio economic status, “T” statistics is administered. The following table shows results of Mean differences and t-test. 151

ISBN: - 978-93-88936-09-5 Table 5.19 Socio economic status of NR distributors in Kanyakumari district Kalkulam taluk Vilavancode taluk Factors Type of distributors Age Education Mean difference “t” statistics Mean difference “t” statistics Mean difference Thovalai taluk “t” statistics 1.75714 15.401 1.78000 13.196 1.80000 9.567 3.28571 19.560 2.84000 17.210 3.00000 13.654 2.60000 15.228 2.88000 14.307 2.53333 11.082 Marital status 3.92857 16.535 3.56000 14.004 3.50000 10.147 income Family size 2.67143 18.080 3.02000 15.672 2.48485 12.424 Nature of family Types of subsidy 3.24286 14.007 3.54000 13.192 3.76667 10.823 3.80000 20.067 3.56000 15.804 3.40000 11.129 Time spent 4.15714 16.929 3.92000 14.088 4.00000 11.569 3.67143 14.648 4.14000 14.712 4.26667 10.146 Marketing area 4.05714 19.285 4.30000 16.906 4.06667 12.796 Sources of finance 2.81429 21.788 2.98000 18.889 1.96667 13.320 Source: Primary data As seen in the above table the highest mean difference of the Kalkulam taluk, Vilavancode taluk and Thovalai taluks are 4.15714, 4.30000 and 4.26667 respectively. The highest mean differences are registered for the variable types of subsidiaries for two taluks namely Vilavancode and Thovalai and time spent for Kalkulam taluk. Regarding “t” statistics, the significant difference among the socio-economic status of distributors in rubber sheets is identified for the variables viz Type of distributors, and their Age, Education, Marital Status, Income Family Size, Nature of Family, Time Spent For Business, Types of Subsidy, Area of Marketing, and Sources of Finance as the respective “ t” statistics, are significant at 5% level. Paired sample correlation Ho: there is no significance between Types of distributors and problems in storage of rubber distribution in Kanyakumari district. Ho: there is no significance between Mode of transportation and problems in transportation of rubber distribution in Kanyakumari district. Ho: there is no significance between Mode of sales and problems in marketing of rubber distribution in Kanyakumari district The Paired-Samples T Test procedure compares the means of two variables for a single group. The procedure computes the differences between values of the two variables for each case 152 ISBN: - 978-93-88936-09-5 and tests whether the average differs from 0. The following table explains that the three paired sample correlation from the distribution of NR in Kanyakumari district. Table 5.20 Paired sample correlation Pairs 1. 2. 3. Factors Types of distributors and problems in storage Mode of transportation and problems in transportation Mode of sales and problems in marketing Kalkulam taluk .792 .867 .866 Vilavancode taluk .835 .888 .870 Thovalai talluk .808 .859 .858 Source : primary data The above table explains the relationship between types of distributors and problems in storage applied in paired sample correlation for three taluks in Kanyakumari district. In Thovalai taluk shown results as .808 higher positive correlation likewise in case of mode of transport and problems in storage, mode of sales and problem in marketing also shown as higher positive correlation are .859 and .858 respectively. It signifies that in three taluks, namely Kalkulam taluk, Vilavancode taluk, and Thovalai taluk , the paired sample correlation values reflects highly positive correlation, since the results found is above 0.5 per cent, all pairs are significant. Marketing available for rubber producer The following five channels were identified in the marketing of natural rubber. i) Grower ii) Grower - Manufacturer - Local Merchant iii) Grower - Local Merchant - Manufacturer iv) Grower - Wholesaler v) Grower - Co-operative society - Manufacturers - Wholesaler - Manufacturer - Manufacturer SUMMARY Rubber passes from the production center through different channels to the manufacturer.The rubber sheet produced by the cultivators are sold either directly to the manufactures of rubber products who are the ultimate consumers or through local merchants, whole sales and growers co-operative marketing society. This chapter deals with domestic and international trade, price, consumption, balance of trade etc.,. The majority of the respondents make their distribution through village traders because the respondents received input materials and advance loan from the village traders. 153

ISBN: - 978-93-88936-09-5 154 ISBN: - 978-93-88936-09-5 CHAPTER - VI FINDINGS AND SUGGESTIONS 6.1 INTRODUCTION In this chapter, at attempt has been made to recapitulate the major findings of the present study and it has cited a few suggestions for eliminating the problems associated with the rubber cultivation and marketing of natural rubber in the study area. In the previous chapters various aspects of the rubber growers have been explored and explained. The entire study was based on both the primary and the secondary sources of information. It was covered in three Taluks - Kalkulam, Vilavancode, and Thovalai in Kanyakumari based on three categories of rubber growers namely small, medium and large size of land under rubber cultivation. This study reveals that the production and marketing of rubber in Kanyakumari District in purely explorative in nature. The findings, suggestions and conclusions arrived at are listed below: 6.2 MAJOR FINDINGS OF THE STUDY 6.2 (A) Findings related to secondary data • India is currently the sixth largest producer of NR in 2015 with a share of 4.7 percent of world production. During 2015, the output in main producing countries viz; Thailand, Indonesia, Malaysia and Vietnam increased, whereas production in China and India decreased during 2015. Global SR production during 2015 was 14.46 million tonnes as against 14.179 million tonnes in 2014, registering an increase of 2.0 percent. There is a highly positive correlation between total world rubber production of main countries and the study period from 2001 to 2015. • Consumption of natural rubber increased from 7333 tonnes in 2001-02 to 12137 tonnes in 201415, except in 2009-10. Consumption of synthetic rubber also increased from 10253 tonnes in 2001-02 to 19984 tonnes in 2014-15, except in 2008-09. Increase in the economic development in the emerging economies like China and India had played a significant role for the increase in the consumption of rubber both natural as well as synthetic rubber. The highest mean score of Consumption of Natural rubber in the world for the study period registered is 1168 tonnes in 2014. • The total production of natural rubber in the country and RSS grades of rubber sheets obtain the value is 0.999. It is a highly positive correlation. • The total rubber area is increased and automatically affects the other variables like tapped rubber area, production and average yield/ha also increased. In the year 2013-14 and 2014-15 155

ISBN: - 978-93-88936-09-5 the rubber area were decline due to the natural calamities (heavy rain) also affect the total production are 844000 tonnes and 645000 tonnes respectively. • The tapped area of rubber has gradually increased from 400713 hectares in 2001-02 to 521653 hectares in the year 20014-15. The significant difference among the variables of NR in India are identified Total Area (ha), Tapped Area (ha), Production (Tonnes) and Average yield/ha (kg) as the respective “T” statistics are statistically significant at 5 per cent level of significance. • During the study period, from 2001-02 to 2014-15 all the years except 2002-03, 2003-04, 200506, 2013-14 and 2014-15, the balance of trade is negative, because of imports exceeds exports. • As per the country wise export information of Natural Rubber during the study period from 2001-02 to 2014-15, the leading export market of Natural rubber are China, Malaysia, Indonesia, India, Turkey, Sri Lanka, Spain, Pakistan, Singapore and Nepal. In 2011-12, export of NR decreased to 27,145 MT as compared to 29,851 MT in 2010-11. • Rubber cultivation in India has been traditionally confined to the hinterlands of southwest coast, mainly in Kanyakumari district of Tamil Nadu and Kerala. Kerala and Tamil Nadu together constitute the traditional rubber growing regions in the country. Kerala alone contributes 89 per cent of the total rubber produced in India and an area of 534,228 ha is under rubber. Tamil Nadu contributes another 3 per cent of the total natural rubber production. • The North‐Eastern region contributes upto 5 per cent of the total production, while Karnataka contributes 3 per cent. An area of 113,685 ha is covered by rubber plantations in the north‐eastern region. • The major crop in and around Kanyakumari is rubber. The paddy area and coconut garden have been converted into rubber estates. Because the climatic condition and soil conditions in Kulasekharam and Thovalaitaluk are suitable for good quality of rubber, water which is very important for the growth of rubber is available in these areas throughout the year. 6.2 (B) Findings related to cultivation or growers: • The majority of the sample growers in the Kanyakumari district belong to the age group 30-40.The calculated value is (35.4) more than the table value is (3.84). Hence, the hypothesis is rejected. Therefore, there is significant relationship between the age wise classifications in the three selected taluks. • The total respondents are 300 of which male respondents are 70 per cent (210) and female respondents are only 30 per cent (90). The Majority of the respondents are male. They are dominating NR cultivation. The calculated value is (48.6) more than the table value (3.84). Hence, the hypothesis is rejected. Therefore, there is significant relationship between the sex wise classifications in the three selected taluks. 156 ISBN: - 978-93-88936-09-5 • In Kalkulam taluk 130 respondents are educated, and as a few as 10 are illiterate. In Vilavancode taluk 98 respondents are educated and remaining 12 are illiterate. In Thovalai taluk 43 respondents are educated, and the rest of them are illiterates. • The Majority of the respondents in the three taluks are married, and they have more responsibilities and strong influences as to do anything for their earnings. • A maximum of 66 per cent (200) respondents are full time cultivators and other 44 per cent (100) comes under the category of part time cultivators. It reveals that the Majority of the respondents are full-time cultivators and their earnings solely depend on NR cultivation. • The study brings to important factors that determine the preferences for NR cultivation by the growers. It includes family cultivation/ availability of land for cultivation, unemployment, climatic condition, self-interest/ attractive price and easy marketability. • Only a few of, 65 respondents (22) are using non-organic fertilizers for the cultivation of natural rubber. • This study exhibits that among the total distribution, 43 per cent (130) have fruits, 21 per cent (62) of the respondents have spinach, 19 per cent (59) have vegetables and at least 17 percent (50) have flowers as intercrops to rubber trees. It is understood that the majority of the respondents have preferred intercrops as fruits. • The majority of the respondents feel that the quality of rubber produced in Kanyakumari district is one of the best qualities in the world and the yield per acre is also very well compared to the other parts of India. • The findings reveal that 46.5 per cent of the (140) respondents have used smoke houses, 26 per cent (77) have used sun drying, and 18 per cent (53) use partial sun drying and the least, 10 per cent (30) use kitchen drying for rubber sheets. A minimum number of respondents are using kitchen drying for rubber sheets, because they have less than 2 acres of land for cultivation of rubber. • This study examines that the 48 per cent (145) are tapping the trees by professional tappers, 22 per cent (66) by all, 20 per cent (59) by their family members and 10 per cent by him/her. • It could be understood that the majority of the respondents are using plastic cups, which are easily available in the market and have a long usage and quite durable. • The Majority of the respondents are feels that lack of availability of skilled labour and tappers. These factors might be affecting the production of rubber. Hence, in modern day’s new technology adopted for tapping and planting rubber trees. So the awareness and training should be given to them. 157

ISBN: - 978-93-88936-09-5 • The major problems encountered by the rubber producers in the local market. The most important problem is the lack of infrastructure facilities for purpose of keeping up stocks of smoked rubber sheets and fresh latex. This handicap has provided itself to be major drawback for the rapid growth of rubber production in Kanyakumari District. The rubber producers who are marginal in size can become better. When they are given proper storage facilities to store the latex which they are produce every day. • The lower profitability on fresh land rubber can be attributed to the enormous cost involved in preparing and planning rubber trees in such areas. • Rubber production being highly expensive the annual saving per producers is very low. Because of this fact, that the producers are in untapped areas have borrow huge amount of money. • The largest single expenditure among the rubber producers is an manuring the purchase of tools and instruments another major expenditure related to the production of rubber expenses of labour shows the tendency of increase in the amount with the increase in the area of production. • Kendall’s co-efficient of Correlation method is used for the relationship between varieties of cultivation and factors for NR growers in Kanyakumari district. This study reveals that all the factors have the results of variables which are more than 0.5 except own machine, shows the results 0.395, 0.316, and 0.273 respectively, hence it is a highly positive correlation. • The ANOVA value is found to be significant at five per cent level. This shows that the regression equation framed is a good fit. The ANOVA value of Kalkulam taluk indicates around 152.757, Vilavancode taluk indicates that the value is 222.164 and Thovalai taluk indicates that the value is 98.897 of the variations in sources of the NR production due to the selected variables. 6.2 (C) Findings related to marketers/distributors: 1. Most of the respondents (79 per cent) are using non-banking funds, as they feel that they charge high rate of interest and plantation property as a security. 2. Out of the 150 distributors, 40 have opined that the labour charge is high, 33 face packages, 23 face lack of storage facility, 21 loss of weight, 17 deterioration of quality, and 16 face grading problems. 3. Only a few distributors (7per cent) are of the opinion that the package and licence problems are common in distribution of NR in Kanyakumari district. 4. The findings of the study relate to the Sale of Rubber Sheets through Commission agents. The reasons are credit facility, prompt payment, long term practice, low risk, better prices 158 ISBN: - 978-93-88936-09-5 and availability of inputs. Majority of the respondents are feeling to sell their rubber sheets for getting advance money (credit facility) from the commission agents. 5. Ranking method is used for the problems in transportation. It is clear that loading/unloading and Non-availability e of vehicle were the main problems faced by the cultivators, which secured 38 per cent mean score. Quality adulteration was the third most problem that secured 34 per cent mean score, and taxability has secured 28 per cent was the least problem faced by the cultivators. 6. Ranking method is used for the problems in marketing cost for rubber sheets. It is clear that the first rank was received for transportation (M=3.78) per cent, followed by second rank towards cheated by middlemen (M=3.73), and finally the eighth ranks for abnormal loss (M=3.11). 7. Ranking method is used for the Reasons for sales through commission agents. It is clear that the first rank was received for credit facility (M=3.79) per cent, followed by second rank towards long term practice (M=3.75), third rank towards low risk (M=3.58), and finally the seventh rank towards provide input. (M=2.31). 8. Ranking method is used for theMaintenance cost of rubber sheet. It is clear that first rank was received for sheet drying (M=3.43) per cent, followed by second rank towards operation cost (M=3.40), third rank towards fuel expenses (M=3.30), fourth rank towards cost of vessels (M=3.19), fifth rank towards electricity charges (M=2.80),and finally the sixth rank towards land tax. (M=2.68). 9. Ranking method is used for the Reasons for sales through wholesales. It is clear that first rank was received for No Transportation cost (M=3.78), followed by second rank towards No storage cost (M=3.65), and finally the seventh rank towards better prices (M=2.74). 10. There is a significant a relationship between distributors and the problems faced by the distributors in the marketing of rubber sheets in Kanyakumari district. Since the P values for transportation in three taluks, Kalkulam, Vilavancode and Thovalai are0.002, 0.043 and 0 .044 respectively. Thus it is concluded that there is significant relationship between the distributors and the problems in marketing of rubber sheets in Kanyakumari district. 11. It is found that the P value is lesser than .05, thus the alternative hypothesis H1 isaccepted, and other problems like storage, sheets purchase and market, also shows that the P value is greater than .05 per cent , then the hypothesis is rejected but the null hypothesis is accepted. 12. To ascertain the significant difference between the NR distributors based on three taluks in Kanyakumari district and their socio economic status, “T” statistics is administered. The highest mean difference of the Kalkulamtaluk, Vilavancodetaluk and Thovalaitaluks are 4.15, 4.3 and 4.26 respectively. 159

ISBN: - 978-93-88936-09-5 13. Regarding “t” statistics, the significant difference among the socio-economic status of distributors in rubber sheets is identified for the variables viz Type of distributors, and their Age, Education, Marital Status, Income Family Size, Nature of Family, Time Spent For Business, Types of Subsidy, Area of Marketing, and Sources of Finance as the respective “ t” statistics, are significant at 5per cent level. 14. The paired sample correlation values reflect highly positive correlation, since the result found is above 0.5 per cent in three taluks, namely Kalkulam, Vilavancode, and Thovalai. 15. The rubber market in Kanyakumari District is dominated mostly by the retailers. They are assisted by brokers and intermediaries. They are operated mostly in black market, operations, and in order to avoid the excise duty imposed in latex, the wholesaler, who are deals in their rubber products, are marketed in metropoliticion cities like Chennai, Mumbai, and Calcutta. 6.2 (D) Findings related to NR Tappers: 1. The majority of the sample tappers in the study area belong to the age group 30-40.The study shows that the new generation is averse to rubber tapping. Out of 150 sample tappers only 10 per cent come under the age group, below 30. 2. The incomes from agricultural sector and labour sector of the tappers are ever on the rise. This is based on talukwise classification in Kanyakumari district, raised in their sources of income. While, in the Kalkulamtaluk, the income group share of different sources is as follows: tapping 47 per cent, agriculture 17 per cent, animal husbandry 24 per cent, casual labour 3 per cent and drivers 9 per cent. 3. The majority of the respondents are tapping the trees in smallholdings, and some of them prefer to tapping the trees in Arasu rubber board, because they enjoy more monetary and non-monetary privileges like education scholarship, medical facility, headlight, monthly salary received, and housing facility etc., than the others. 4. The term ofremuneration paid to the tappers includes monthly basis, daily/weekly basis, rate per tree basis, and both rate per tree and non-rate per tree basis. Basically, they follow two methods of payment to the tappers, piece rate of tree and non-piece rate of tree.The majority of the NR tappers are received payment on the basis of piece rate per tree tapping, because in Kanyakumari district NR cultivation in smallholdings. 5. Educational status of the family members as a whole and that of tappers has been analysed. More than 92 per cent of the sample respondents are literate. Out of the 150 respondents 8 percent are illiterate. Nearly 18 per cent have dropped their study at the primary level, whereas 26 per cent discontinued study at secondary level, 14 per cent tappers are 160 ISBN: - 978-93-88936-09-5 completed higher secondary and remaining 30 per cent tappers are graduate. It signifies that majority of the tappers are literate in the study area. 6. An average time of 3 hours is taken for tapping; 1 hour is taken for collection of latex and 0.45 hour is taken for rolling. Almost all the cases drying and smoking of the sheets are done by the owner of the land himself. In some cases the owner himself may manage all allied works of tapping. 7. The majority of the NR tappers received payment of their advance money on weekly basis , because they receive remuneration calculated tapping per tree in Kanyakumari district. 8. Only 46 per cent of the tappers felt that the functioning of the Government support is poor. Among them 31 are in Kalkulam taluk, 25are in Vilavancode taluk and 13 are in Thovalai taluk. 9. Male tappers dominate the field of tapping. Female tappers are interested more in estate tapping because there is no gender discrimination regarding wage or allowances. 10. Only a few tappers are in the study area facing monkey bite, snake bite, elephant destruction etc., 11. In Kalkulam taluk, majority of the tappers receive low charges or low wages, and nearly 20 per cent of the tappers are facing the problems like non-seasonal cultivation of rubber in Vilavancode and Thovalai taluks . 12. A number of tapping families enjoy financial support for constructing houses and improving residential facilities from Housing Board and other governmental and non- governmental institutions 13. Out of 150 respondents, 57 per cent of the respondents are getting advance money from the government or owner of the garden and the remaining 43 per cent of the respondents are not getting advance money from the government or owner of the garden. 14. A five point questionnaire was framed to assess the opinion regarding the Training programmes attended by tappers in Kanyakumari district. It was found that the majority of 26 per cent of the tappers have expressed satisfied and moderate the Training programmes. 15. A least of 16 per cent of the tappers felt satisfied and very poor in the nature of the assistance of rubber board in Kanyakumari. 16. The experiences of NR tappers in Kanyakumari district and its classification into three taluks are as below: Out of total respondents, 82(70 per cent) are having the experiences of above 10 years. 17. Only the least number of the tappers, 34(25 per cent) have earned a monthly income of below Rs. 20000/--. 161

ISBN: - 978-93-88936-09-5 18. General health condition of women tappers is very poor because of their continuous engagement in household works or tapping from dawn to desk. Moreover, the gravity of occupational physical problems such as back pain, chest pain, leg pain etc. is high among them 19. The majority of the respondents feel satisfied about the tapping of the rubber trees because skilled tappers are available there. 20. The entire co-efficient of correlation is found significant at 5 percent level. Among the independent variables, repayment, remuneration, advanced received, sources of finance and sources of income, and the dependent variables include age, and sex, education and experience are found to have positive influences. It implies that there is a close relationship between dependent and independent factors of tappers in Kanyakumari district. 21. The paired sample correlation applied for 10 pairs of variables, their values reflect a highly positive correlation, since the results found is above 0.5 per cent in three taluks, namely Kalkulam taluk, Vilavancode taluk, and Thovalai taluk,Thovalai taluk has shown results as 0.964 higher positive correlation likewise Vilavancode taluk and Kalkulam taluk have shown as higher positive correlation that are 0.907 and 0.934 respectively. 22. Regarding the calculated value of all the pair’s t-test is greater than 0.01 levels. So the null hypothesis is accepted at 1 per cent level. It can be concluded that there is no significant differences. In Kalkulam taluk from pair 1 to pair 7 and pair 10 are accepted, and the remaining pairs 8 and 9 are rejected. In Vilavancode taluk, from pair 1 to pair 3, pair 6, pair 8 and pair 9 are accepted, there is no significant among the pairs, and remaining pair 4, pair 5, pair 7 and pair 10 are rejected. In Thovalai taluk, from pair 4 to pair 9, pair 2 is accepted, as there are no significant differences among the pairs, and remaining pair 1, pair 3, and pair 10 are rejected, because there are marked differences. 23. Out of the total sample tappers, 93 percent are full time tappers and 7 percent are farmers or small-scale businessmen rather than tappers. They engage in tertiary sectors also. 6.3 SUGGESTIONS OF THE STUDY 6..3 (A) Related to production  The gestation period of the plantation locks up the initial investment till the commencement of the economic yield. Shorter gestation period varieties of plants are to be evoked for quick recovery of returns of investment in order to attract more producers.  Unorganized sector is a major constraint in increasing the holders’ income. Enhancing the net farm income of rubber holdings is the prime factor. Intercropping is secondary source of income, rubber wood, honey from rubber, rubber seed oils etc., these earnings are also supported to them. 162 ISBN: - 978-93-88936-09-5  Soil conditions are good and larger number of holdings of smoke size. The poor remain poor as they are unable to utilize their land source to the optimum. Climate change has its impact on plantation. High temperature causes about 15 per cent fall in rubber production.  The yield per hectare, which is the productivity of rubber plantation, is at this peak in Kerala, growing faster in Karnataka but slow movement in Tamilnadu.  Rubber planting of proven and improved hybrid varieties must be easily to the rubber growers through recognized nurseries at subsidized rates. It can be pest resistant and drought-tolerant. They must be suitable to cultivation in high attitudes and adoptable to different agro climatic conditions.  Empowering small holders with modern technology is a well said answer to increase productivity.  The growers must be educated on the scientific methods of intensive cultivation to increase productivity by periodical “Growers Meet” organized by government organizations and extension agencies using all the popular media of communication.  Liberal financial assistance can be made to the growers as crop loan and development loan through commercial banks and co-operative societies during their needy time.  Warehouses must be established in the production centre. The growers should stock their rubber sheets to sell it at an attractive price at the appropriate time.  The Government should introduce new varieties of rubber plants and new techniques of rubber production through the rubber board so that the employment conditions of rubber tappers would improve well.  The rubber cultivators are more interested in the timely caring of rubber trees because of the continuing higher prices for rubber sheets in the international market. They are also interested to replant and new planting of rubber trees in the district.  The growers who undertake replanting are allowed to raise other crops during the immaturity period, under the Replanting subsidy scheme of the Rubber Board. These inter- cropping is allowed with a view to providing the grower with a means of living, during the immaturity period.  A detailed study of the economics of intercropping will be effective to convince the replanting growers of the most advantageous combination of crops that could be grown in rubber areas.  The Government should also give adequate loans for the production of rubber as well as for the welfare of rubber tappers to improve their standard of living.  The growers have little, if any, access to modern plantation materials and do not follow agricultural practices that maximize crop. Furthermore, they often lack sufficient funds to fertilize. 163

ISBN: - 978-93-88936-09-5  They are dependent on family labour, which limits the scale and productivity of the farming undertaken.  They tend to have poorer land and uncertain land tenure (early smallholders emulated activities on nearby estates and planted rubber trees on cleared forest land to which they did not have title). In subsistence agriculture, a formal title to the land may be rare, especially where the law refers to customs, and communities decide in practice on individual land occupancy (also, entitlement to land may be restricted by legislation).  As Rubber production being highly expensive the annual savings per producer is very low. Because of this fact the producers in the untapped areas have borrowed huge sum of money.  The largest single expenditure among the rubber producers is manuring the purchase of tools and instrument. Another major expenditure related to the production of rubber expenses of labour shows the tendency of increase in the amount with the increase in the area of production. 6.3(B) Suggestion related to tapping • Generally, tapping is a process of controlled wounding during which the shaving of bark is done. The important factors responsible for harvesting a rubber tree are the tightness of opening, slope and direction of tapping cut, skilled tapping is necessary for the good health of maintaining the longevity of its production period. From the detailed analysis about the rubber tappers in Kanyakumari district the following useful suggestions have been made. • Adoption of rain guarding is said to have raised the number of working days. As only below 70 per cent of tappers are working in rain guarded holdings. So early tapping can be done. Moreover, rain guards can be used to increase output by 11 per cent. • Most of the tappers (men) used liquor after tapping, so that a major share of their income is spent on it followed by travel, tea, coffee, chewing, smoking and playing cards. • The Government should encourage the involvement of female tappers through the Rubber Board in order to earn more income for the family. • Since most of the employees are above 45 years of age, there may not be non-availability of employees for tapping in future. The major sources of income are tapping, casual labour, agriculture and animal husbandry. Household have to depend on sources other than tapping for the betterment of their standard of living beyond a level. • The employer/tappers should be properly motivated by the owner of rubber estates to save more money in different savings mode. • The employee/tappers should give proper training to make them more efficient in order to get good yield as well as to protect the rubber trees. 164 ISBN: - 978-93-88936-09-5 • At present there is only scarcity of skilled tappers. But the trend shows that in the future the scarcity of tappers, whether skilled or unskilled will becomes chronic as the younger generations do not depend on tapping for their livelihood. • Social customs and traditions force not to engage in tapping. This is due to their lower family income, low educational standard and difficulty in getting other opportunities. • The rubber tappers should adopt the latest scientific methods in rubber latex collection recommended by the Rubber Board. • Above all the rubber estate owners should provide free medical, educational, accidental and all kinds of risk, aids to rubber tappers throughout the year for the welfare of these workers. • Tapping labourers of smallholdings will come under Minimum Wages Act. The average wage rate paid in the study area is not different from that fixed under the Minimum Wage Act. But incentive as over kilo rate for latex prescribed by the Act is unknown to the study area. • Majority of tappers belong to the age group 40 years and more. They are not averse to tapping. But it is very difficult to train them in scientific methods of tapping because of their inflexibility out of overage, personal bondage with growers and their belief that they are 'masters of tapping'. On the contrary, the younger generation is averse to tapping which is reflected by very low percentage of tappers below the age of 30 years. Hence proper steps should be taken by the Rubber Board to involve the younger people in Rubber tapping. • Tapping is considered a job of low social status, especially in the case of younger generation. So they are always trying to get out of the work on the earliest opportunity. Only due to the absence of suitable job opportunities they continue. But the long engagements in tapping, ignorance of other fields, lack of physical abilities etc. of the elderly tappers make them unsuitable for other opportunities 6.3 (C) Suggestion related to women tappers • In smallholdings of rubber the participation of women in tapping labour is very low. Along with the nature of crop, economic backwardness of family, social factors, the approach of rural households to tapping job, the wage rate etc. are also to be considered as determinants of demand and supply conditions of women tappers. • Male domination in tapping is obvious it is clear that the need for employing females arises under a condition of shortage of male labour as a result of their out migration and increase in demand for tappers. The participation of women in tapping seemed to be very low. • All the women tappers are above 40 years of age and married. It shows the approach of the society to the job of tapping. Social customs and strong traditions force unmarried women 165

ISBN: - 978-93-88936-09-5 not to participate in tapping. People believe that persons of low status, especially in the case of women tappers, carry out tapping. • Actually they enter the field of tapping only when it is indispensable for keeping the standard of living of the family even at the minimum level • As far as tapping is concerned, performance of women tappers is better than that of men. However, most of the growers do not prefer women for tapping mainly due to the peculiar nature of work. It is necessary to commence tapping early in the morning, as late tapping will reduce the exudation of latex. The suitable time for tapping prescribed by Rubber Board is 4 A.M to 7 A.M. In spite of their best efforts, women tappers cannot reach the holdings within this time, because they have to attend the household works before going for tapping 6.3 (D) General Suggestions related to the Government • It has been suggested that an awareness has to be created among the growers of rubber about disease of tree, tapping, availability of bank loan, market price, quality of rubber, export of rubber sheets etc., Awareness can be created by preaching on staging mini-dramas, conducting conferences, debates, symposiums, seminars and workshops. • A large number of rubber smallholders are subsistence farmers working in small family groups. They tend to face many problems, which minimize their chance of achieving good economic returns from their holdings. • Computerization of data should be made available in rubber board regarding the rubber status of Kanyakumari District. The government should introduce a package of programmers who can make available all kinds of credit and storage facilities to the dynamic business in Kanyakumari District. • Until the rubber plant reaches the stage of tapping Govt. should bear part of cost in preparing the land and planting the rubber Govt. schemes shall be made available only to those producers who are ready to spend sufficient time for production. • The government should come forward to offer encouragement to retails and whole sales who are interested in setting up rubber business in Kanyakumari District. By simplifying the formation involved in granting licenses to the rubber they can accomplish the task. • The government should introduce a package of programmers which can make available all kinds of credit and storage facilities to the dynamic business in Kanyakumari District. 6.3 (E) Suggestions related to marketing Indian rubber sheet may be more attractive than foreign consumer by personal selling and also by arranging stalls in international trade fairs. 166 ISBN: - 978-93-88936-09-5 • The government should come forward to offer encouragement to retailers and wholesalers who are interested in setting up rubber business in Kanyakumari District. By simplifying the formalities involved in granting licenses to the rubber they can accomplish the task. • Efforts should be made to keep proper records regarding the production and marketing of rubber for further research purposes. • Efforts should be made to start new industries based or rubber production. The availability of the raw materials will generate employment potential for the future generation and unemployed youth. • The prices for rubber sheets are prone to fluctuation. To put it simple stay constant even for a couple of weeks. They keep changing almost every day; this clearly indicates the swelling demand for rubber sheets and the inability of the local producers to cope with it. • In the problem which the rubber markets face is the lack of transport and communication facilities. Hence the need arises as how to connect the rubber producing centre’s of Kanyakumari District, with the marketing centers in the towns and cities far and near also with industries producing rubber products which are located for away from the latex producing centers of Kanyakumari District. The products of latex should be equipped with a better their monetary prospects in the years to come. • Since rubber producers have to wait for getting on income till the tapping starts they should be protected by the government through subsides and loans. Such help will also reduce the necessity to borrow huge amount of money at this stage from private finance. 6.3 (F) Suggestion related to Rubber Board • The Rubber Board may perform the functions of adviser to the Government by collecting the statistics of production, consumption and stocks from estates, dealers and manufacturers and make projections for future supply and requirements. • Active encouragement is given by the Rubber Board in the form of technical and financial assistance for processing rubber into better quality sheet through a network of extension offices. 6.4 CONCLUSION The production of rubber in Kanyakumari District stands far above the other district because 90 per cent of the rubber plantations are located in the district. Natural rubber is the backbone of commercial agricultural scenario of the state and rubber plantations has profound influence on the economic and social status of the people. This study was conducted with the aim of learning about the production and marketing of rubber in Kanyakumari District. It is helpful to the authorities 167

ISBN: - 978-93-88936-09-5 concerned as they will come forward to implement suggestions for solving the problems of rubber producers in Kanyakumari District. The government needs to attend to the problems of the Indian rubber producers. Rubber industry plays a major role in the economic development of the nation. Over 20 million families, mainly small growers, many with holdings of two hectares or less are dependent on rubber cultivation for their livelihood. The state support to the rubber industry needs to be regarded as a social security. Rubber industry is an economically vital sector based predominantly in rural areas of the country. Rubber market in 2016 continues to be driven by moves in other commodity prices and fluctuation in currencies. NR producing countries are working on short to long measures to ensure an orderly growth of rubber supply that matches emerging trends in the world demand. Production of Hevea rubber is sustainable if supported by continuous improvement in productivity, efficiency and quality. 168 ISBN: - 978-93-88936-09-5 169

ISBN: - 978-93-88936-09-5 Bibliography I. Books: 1. B.C.Sekhar, Rubber price come down commerce, November 6, 1992. 2. Block statistical hand Book 2005-2010. (Kuzhithurai) 3. District statistical hand book of Kanyakumari district 2005-2010. 4. Rubber board 2003, 2008. (Marthandam) 5. Rubber statistical vol 241 page 4,5,13,14 6. Burger,K.Haridasan, V.Smit, H.P.Unni, R.G. and Zant W (2000) The India Rubber Economy, History. 7. Gol (1999) Export and import policy 1 April 1997-31 March 2005. Ministry of commerce government of India-New Delhi P-65 8. SreeKumari B, Harihasan.V and Rajesh Karan’s (1990) from gate price of N.R. 9. Rubber statistical bulletin. International Rubber study group London. 10. Price movement of an agriculture raw material with Inventory adjustment-case of Indian National rubber in 1970 (mani-SC1994) 11. Kanniyakumari district Gazetteer Chennai, Gazetteers of India, and Government of Tamilnadu. 12. Indian Rubber Statistics Rubber Board Kottayam Vol.21, 1996.p.23 13. Indian Rubber Statistics Compiled from Indian Rubber Statistics, Rubber Board of India, 18. Indian Rubber Statistics, Vol.31, 2009 Rubber Board of India, Indian Rubber Statistics, Vol.31, 2009. 14. S.Mohankumar and TharianGeorge.K. Indian Rubber Products Manufacturing IndustryEvolutionary Dynamics and Structural Dimensions, Rubber Research Institute of India,1999. 15. Mahalingam N., "Rubber Industry Implication of Economic Reform Policies”, Kisan World, Vol. 31, No, 12, December 2004. 16. Tharian George K., Toms Joseph Joby Joseph, “Natural Rubber in Post-QRS Regime,” Economic and Political Weekly, Vol. XXXVII, No. 32, August 10, 2002. 17. KarthikakuttyAmma M. and et.al., The Rubber Board Bulletin”, Vol. 27, No.4, Kottayam, JuneAugust 2000. 18. Kohts R.L., and Uhi J.N., “Marketing of Agricultural Products”, Macmillan Publishing Co., Inc., New York, 1980. 19. Krishna Kumar, A.K., Rubber Mooting Cost – Effective Measures, The Hindu Survey of Indian Agriculture, Annual Hindu Publication, Chennai 1994. 20. 21. Impact of LPG on Natural Rubber and Rubber-based industries in Kanniyakumaridistrict - v. Janet Y. Selvia. Indian rubber statistics Compiled from Indian rubber statistics 22. Rubber Board of India, Indian Rubber Statistics, Vol.31, 2009 23. K.J.Mathew, “ promise of small holdings” Pg. no.45. 24. “Rubber industry companion“ Kuryan and Kuryan ., Phillips publishers Kottayam 1996. 25. India rubber statistical 22:9:11 26. Rubber Growers Guide-2014, Rubber Board, Kottayam, p.52. 27. Asian Rubber Handbook and Directory, 2012, pp.65-70. 28. 26th Annual Report, Arasu Rubber Corporation, 2014. JOURNALS: 1. Kissan world March 2008.vol35 No.03 AmbalMukharjee 2. Kissan world March 2000 vol 27 No.3 p.22 3. Kissan world De.4-2004 vol31.No.12(S.No.1318, S.No.19112)St.Joseph’s college (Autonomous)Thiruchirappalli. 4. E.Dharmaraj “Natural rubber production in India” Kissan world vol 9 No.12 Dec 1992 page 31. 5. T.Paul raj chatlenges of rubber industry in India “southern economist vol 35 No 15,16 Feb 1996 p.23. 6. R.Mohan Kumar “opportunities for Indian national rubber industry southern economist act 1 1997. 7. Lal Wilson “Rubber cultivation” Kissan world March 1999. 8. Rubber and its cultivation -2005 published by the rubber board(Govt of India , ministry of commerce) Kottayam. 9. Padmanabhan.S (1976), the forgotten history of land KumaranpathupkamNagercoil. 170 ISBN: - 978-93-88936-09-5 10. Aravindakahan Nair, K.A. “processing of the crop from plantations”, Rubber and its cultivation published by Rubber Board Kottayam. 11. BadarAlamIqbal, “More land for Rubber” commerce vol,141, No 3602, July 5, 1980. 12. Jacob Thomas. K “Steadty step in Rubber production”, Yojana, vol-37 No.9, Oct 31, 1993. 13. Kulkarni. D.S, “ Natural rubber science in India” Commerce, vol.145,No-3726, Nov 6, 1982. 14. Kuttaiah, “plantation operation a success”, Rubber Asia, Vol,7, No-2, Nov-Dec 1994. 15. Manu.M.Patel, “Nitrite Rubber Products, Indian know How”, commerce, vol.133 No 3414, Oct 30, 1988. 16. Mathew. J.J, “Rubber Plantation Industry A Success Story”, Kisan World. Vol.22, No.3 Mar 1995. 17. Mookerjee.K.N, “Heavy Demand calls for substantial Expansion”, commerce vol.117 No 2983, Jul 6, 1968. 18. Muthanna .G, Rubber Asia, Mar- Apr 1998 Vol.12, No.5. 19. Patel.N.N.K, “problem of planters plea for a high power committee”, commerce vol.cx111, No.2887, sep 3, 1996. 20. Rama Krishna Sarma, “Kerala plantation Industry”, Kisan world, Vol.6, Feb 5, 1991. 21. Sundar.R, “Rubber bounces vigorously”, Industrial Economist, volxxxv11.No-5, Aug 1995. 22. Goldar.B.N “Productivity growth in Indian industry New Delhi, Allied Publication 1986. 23. Rubber Asia July, August 2006 24. The Hindu Survey of Indian Agriculture 25. 26. Indian Journal of Rubber Research Indian Journal of Natural Rubber Research 27. Rubber Industry and its Bright prospectus “The Hindu Feb.28, 2008”. 28. R.M. Stern “Malayan Rubber production inventory Holdings and the Elasticity of Export Supply”. Southern Economic Journal vol.31. No.4 April 1965. 29. “Rubber and its Cultivation” Rubber Board 1974. 30. “Rubber Malar” Rubber Board, Marthandam 31. “Rubber Tapping” Rubber Board, Kottayam, Kerela. 32. “Rubber Processing” Rubber Board, Kottayam, Kerela. 33. “Rubber Plantation” Rubber Board, Kottayam, Kerela. 34. “Rubber Plantation Development scheme” Rubber Board, Kottayam, Kerela. 35. “Rubber board bulletin” Rubber Board, Kottayam, Kerela. 36. “ Labour welfare scheme of the Rubber Board 1999 - 2003” Rubber Board, Kottayam, Kerela. 37. “Crop protection in Rubber” Rubber Board, Kottayam, Kerela. 38. “Rubber Plantation Insurance ” – National Insurance Co., New Delhi. Reports and other Publications 1. Aerostat joint Director of Agriculture Kanniyakumari District, 2007 2. Annual credit plan for Kanniyakumari District lead Bank cell IOB 2008-09 3. Annual report , District Rural development Agency Kanniyakumari 2000-2001 4. Census of India, District Census Hand book Kanniyakumari , The director of Census operations part XIII A and B Tamilnadu. 5. Credit plan for Kanniyakumari District Tamilnadu, Indian Overseas Bank, Madras. 6. Rubber Growers Guide 2008. 7. Rubber board, Indian Rubber Statistics Various issues , Kottayam 8. Grower’s companion, Kottayam Rubber Board publication 1988. 9. Rubber and its Cultivation, Kottayam Rubber Board publication 10. Rubber Board Commodity Note Rubber Kottayam Rubber Board publication 1990 11. Rubber Board , Rubber Board BulletionKottayam 2010 12. Rubber Board , Rubber news Kottayam Dec. 1990 and May 1999 13. Rubber Research Institute of India , National Rubber Agro Management and Crop processing, Kottayam 2005. 14. Monthly Rubber Statistical News Vol.74.No.12. May 2016. DISSERATIONS: 1. S.A.Ajai Kumar, Ecological Imbalances and patterns of Economic Development in Kanniyakumari District.(Unpublished M.Phil Thesis), M.S. University, Thirunelveli, Jul 1991, P.75 2. M.StarletHemaMalini, an economic study of Arasu Rubber Corporation. (Unpublished M.Phil Thesis) submitted to M.S. University Thirunelveli. 171

ISBN: - 978-93-88936-09-5 3. 5. 6. 8. 9. 11. Santhose.P“Cost of Cultivation and marketing of rubber on corranore District Kerala”, unpublished M.Sc(Agric) Thesis submitted to Kerala Agriculture University”, Thrissur 1985. 4. Ushadevi .T.V, “Technology Adaptations in rubber production in Kerala”, Unpublished PhD Thesis, M.G University, 1999. S.M. Nathiya, A production and marketing of rubber in Vilavancode Taluk. Unpublished M.Phil Thesis Submitted to M.S.UniversityTirunelveli. S.Sajeena. “A production and marketing of rubber in KanniyakumariDistrict‘ Published PhD thesis submitted by M.S. University Tirunelveli, June 2008.. 7. S. Uma Sheela Impact of plantations on Kerela’s Economy with special reference to Rubber Unpublished PhD Thesis, M.G University, 1999. Impact of LPG on Natural Rubber and Rubber-based industries in Kanniyakumaridistrict - v. Janet Y. Selvia. S.Mohankumar and TharianGeorge.K. Indian Rubber Products Manufacturing IndustryEvolutionary Dynamics and Structural Dimensions, Rubber Research Institute of India, 1999. 10. Dr.TharianGeorge.K, Joint Director (Economics), RRI India and Dr.R.K.Matthan, Polymer Consultancy Services Pvt.Ltd., Chennai for some of the inputs. John, K.K, “Impact of Economic Liberalisation and Globalisation on the Marketing of NR in India,” Ph.D Thesis, Mahatma Gandhi University, Kottayam, June, 2002, p.3. 12. B. Sankaran “ Production and Marketing of Tea in NilgirisDistrict“Published PhD thesis submitted by M.S. University Tirunelveli, June 2007. 13. MaliniHema M. (1993), “An Economic Study of Government Rubber Corporation- A Government of Tamil Nadu Undertaking”, 14. Paul Raj (Dec. 1996), “Challenges of Rubber Industry in India”, Southern Economist, Volume 35, Issue No. 15-18, pp. 23-25, Bangalore. 15. 16. 1Ezeeth. P. (2002) “Cost and Profitability Consideration relating to Natural Rubber in Kanyakumari District”, Unpublished Dissertation to ManonmaniamSundaranar University, Tirunelveli. Jeba. J. (2002), “An Economic Study of Rubber Production in Kulaseharam Town Panchayat in Kanyakumari District’, Unpublished Dissertation to ManonmaniamSundaranar University, Tirunelveli. 17. Mathew.K.J, (2006) “focus on cost efficiency” the Hindu Survey of Indian Agriculture 2006 p.85. 18. Shankar Meti, , D.V.K.N. Rao, N. Usha Nair and James Jacob, “Distribution of Natural Rubber Cultivation in Relation to Soil And Landscape Attributes In India”, RB Kerela. 19. Shanley, P., DA Silva. F.C. and Macdonald. T. (2011) “Brazil’s social movement, women and forests: a case study from the National Council of Rubber Tappers”, p.234 20. Rubber Research Institute of India, Rubber Board, Kottayam, Kerala, India 686009E-mail: meti@rubberboard.org.in2. Senior Scientist, IGFRI, Jhansi, UP, India 284003 21. JACOB Thomas, K., “Rubber plantations as a Professional”, The Hindu Survey of Indian Agriculture, Annual Hindu Publication, Chennai, 1992, p.99. 22. N. Suthendren. “Entrepreneurship in Fishnet Manufacturing Units- A study with special reference to Kanniyakumari” Published PhD thesis submitted by M.S. University Tirunelveli, April 2012 WEB SITE 1. Automotive Tyre Manufacturer Association. www.atmaindia.com 2. www.rubber board.org.in 3. www.tnagriculture.gov.in 4. www.indiamart.com 172 ISBN: - 978-93-88936-09-5 173

DBFli F M.Com, SET, Ph.D, MBA ` • Dr.B.Felix Francy, M.Com, SET, Ph.D, MBA teaching and research. She has published 32 research papers in referred journals and has authored a book has visited two countries Malaysia and Dubai. Professor of Commerce in St.John’s College, Palayamko Dr.B.Felix Francy is serving as the Assistant kottai. She has 22 years of experience of ok. She has reviewed two text books. She Dr.B.Revathy, M.C • Dr.B. REVATHY is currently Professor and om, SLST, M.Phil, B.Ed, Ph.D, DCA, MBA, Head, Department of Commerce, Manonmaniam Sundar has contributed 160 chapters of edited volu Doctorate in Commerce under her guidance an m ar University, Tirunelveli, Tamil Nadu. 100 articles in peer reviewed international an journals and 20 in national journals. In additioion, she has published 23 articles in UGC care listed journals and 11 articles in Scopus journals. She es. She has authored 10 books and has edited 6 books and 3 journals. 20 scholars are awarded She has to her credit more than 30 years of teaeaching and research experience. She has published nearly Singapore, Malaysia and Srilanka. She has acted as a resource/chair person in numerous seminars and member of reputed international journals. She d 67 scholars are awarded Master of Philosophy in Comme e is the recipient of three awards. About the Book the Indian economy. Rubber is an important industrial raw material. It tyres. It provides employment to a sizeable population in its agricultural thus contribute substantially to economic prosperity in rubber growing a material. Rubber is an equatorial crop, but under special conditions, i India was the first country in the east to undertake commercial cultivation of natural rubber. Now, India is one among the top ten rubb r producing countries. Rubber plays a crucial role in s of products ranging from toy balloons to giant size e al sector, and a sizeable number makes their living from processing, transporting and marketing rubber goods. Rubber plantations areas. The Indian rubber goods manufacturing industry heavily depends on natural rubber which accounts for 80% of the total raw t provides the principal raw material for manufacturing over 35000 varietie t is also grown in tropical and subtropical areas. It requires moist and hum temperature above 25 degree Celsius. It is mainly grown in Kerala, Tamil Nadu, Karnataka and Garo hills of Meghalaya. Crying Tree '. The reason is that when it is cut off with knife or sharp kitchenware and daily living such as disc, bowl, bottle and simple shoe. such manner several times; they could then get softly underneath shoe Rubber had long been a historical plant. Since Columbus had discovered America in 1495 (525 years ago), Red Indian, ancient tribe of S ols, its natural latex automatically comes out as a tear of tree. Red Indian m to brought them some pieces of Rubber from Para City located on the basin of Amazon River, South America. While staying at home, found that easily. Therefore, name of 'Rubber' or 'Para Rubber' was then recognized by and well-known to public worldwide. The way they made simple shoe was to immerge their foot to natural latex, l for use as wearing a comfortable sock. Before group of European adventu h f South America, called it ' Caoutchoue '. Meaning is ' made the use of Rubber tree and natural latex for their lifted up their leg and waited until it dried. Followed turers leaving South America for hometown, people when they erased pencil trace from paper, it came out Life of people in civilized countries is bound with Rubber from birth to death. Rubber is a significant economic plant. Rubber tree, inclu of use, increase income and employment opportunity for agriculturists. At present, agriculturists are proud to have their plantation, increasing in t after, save cost and time for its growth. Not like other plants, Rubber p o roducts have long been grown with its best outcome year after year. Kozhikode. Years back people used to plant coconut in their fields. Bu Kottayam District have produced good varieties of rubber plants giving India and provides daily income to the growers as well as the worker industrial factories and commercial industries. the world and the yield per acre is also very high compared to other part about 25000 tonnes. Rubber plantations are located in the northern part technique adopted for the survey is purposive sampling. Out of 550 tot the rubber tappers, and 150 respondents are selected from the distributor a The sample for the study comprises of natural rubber growers, distributors and rubber tappers in Kanyakumari District. The quality of rts of Tamilnadu. In Kanyakumari District, natural rubber is grown in about t of the taluks namely Kalkulam, Vilavancode and Thovalai. The total numbe otal sample of the study, 300 respondents are selected from the rubber prod ors. Thus, equal importance is given to the production and marketing of natu economic conditions of the rubber tappers, rubber producers and the m rketers of the rubber products. The study has further identified the factors i study has discussed the problems faced by the rubber producers and marketability of the rubber products. rubber produced in Kanyakumari is one of the best in 35000 hectares and the estimated annual production is er of samples chosen for the survey is 550. Sampling 5 distributors of rubber. Finally it has offered suggestions to increase the p ucers (cultivators), 150 respondents are selected from ral rubber. The present study has examined the socionfluencing the rubber production and marketing. This productivity of rubber products and to enhance the Title :- RUBBER CULTIVATION IN INDIA : PRODUCTION, DISTRIBUTION & TRADE Au hor :- Dr. B. FELIX FRANCY,Dr. B.REVATHY ISBN :- 978-93-8893 Price : 450/t Two important states for rubber plantation include Kerala and Tamil Nadu. Kerala accounts for most of the rubber plantations in India ut today people plant rubber trees instead as it gives a daily income. Rese r ng a very good yield. This plant which was brought to India during the Britis rs. With present rapid growth, most of countries are improving and expand arches made at the research centre at Puthuppally, in ish rule has spread all over Kerala and other parts of ding their business in the field of agriculture, heavy ia and major districts include Kottayam, Quilon, and uding seeds and plantation are beneficial for all kinds otal every year. Plantation is easy to control and look t umid climate with rainfall of more than 200 cm and merce. She has visited three countries viz., nd conferences. She is an editorial board

ISBN : 978-93-85073-24-3 Polymeric Nanocarriers for the Dr.Ram Prakash Aharwal, Department of chemistry, Dr. Hari Singh Gour University ( A Central University), Sagar. M.P. . : He did his post graduation and doctorate in chemistry from Dr. H. S. Gour Central University Sagar . Having more experience in the teaching, Crime investigation as scientific officer in Forensic Science Laboratory Sagar and Bhopal (M.P). He had attend many National and international conferences. He has published many National and international paper. He is also subject expert for board of studies of various universities in India and abroad. He is also working experience in Pharmaceutical industry. Dissolution of Anti-Depressants Drugs by using Kinetic Model Author Dr.T.Lalitha, Dr.R.Saravanakumar Ram Prakash Aharwal Sandeep Kumar Shukla Archna Pandey Published by International Society for Green, Sustainable Engineering and Management ISO 9001:2015 certified for Research, Development &Training An Autonomous Organization, Under Public Trust Act, Government of West Bengal, India Organizational Member Rs.250/Polymeric Nanocarriers for the Dissolution of Anti-Depressants Drugs by using Kinetic Model The Indian Science Congress Association Indian Statistical Institute Quality Council of India ISBN : 978-93-85073-24-3 2019

Polymeric Nanocarriers for the Dissolution of AntiDepressants Drugs by using Kinetic Model FINANCIAL ENGINEERING AND COMPUTATION During the past decade many sophisticated mathematical and computational techniques have been developed for analyzing financial markets. Students and professionals intending to work in any area of finance must not only master advanced concepts and mathematical models but must also learn how to implement these models computationally. This comprehensive text combines a thorough treatment of the theory and mathematics behind financial engineering with an emphasis on computation, in keeping with the way financial engineering is practiced in today’s capital markets. Unlike most books on investments, financial engineering, or derivative securities, the book starts from basic ideas in finance and gradually builds up the theory. The advanced mathematical concepts needed in modern finance are explained at accessible levels. Thus it offers a thorough grounding in the subject for MBAs in finance, students of engineering and sciences who are pursuing a career in finance, researchers in computational finance, system analysts, and financial engineers. Building on the theory, the author presents algorithms for computational techniques in pricing, risk management, and portfolio management, together with analyses of their efficiency. Pricing financial and derivative securities is a central theme of the book. A broad range of instruments is treated: bonds, options, futures, forwards, interest rate derivatives, mortgage-backed securities, bonds with embedded options, and more. Each instrument is treated in a short, self-contained chapter for ready reference use.

Polymeric Nanocarriers for the Dissolution of AntiDepressants Drugs by using Kinetic Model By Ram Prakash Aharwal Sandeep Kumar Shukla Archna Pandey Published by International Society for Green, Sustainable Engineering and Management ISO 9001:2015 certified for Research, Development &Training An Autonomous Organization, Under Public Trust Act, Government of West Bengal, India Member The Indian Science Congress Association Quality Council of India Indian Statistical Institute 2019 © International Society for Green, Sustainable Engineering and Management Editor in Chief: Dr.Debaprayag Chaudhuri, Chairman Production Editor: Mrs.Sonali Chaudhuri Published in India by International Society for Green, Sustainable Engineering and Management 94,Garfa Main Road, Ground Floor, Jadavpur, Kolkata-700 075,West Bengal India Mobile:0091 96 74 76 61 80 Email: isgsem.research.kolkata@gmail.com Website: http://www.facebook.com/isgsem Copyright © 2019.All rights reserved. No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, mechanical or photocopying, recording, or otherwise without the prior permission of the publisher. The society names used in this set are for identification purposes only. First Edition: December’2019 ISBN13 978-93-85073-24-3 (Print with paper binding) 250/

ISBN : 978-93-85073-24-3 Title: Polymeric Nanocarriers for the Dissolution of Anti-Depressants Drugs by using Kinetic Model Ram Prakash Aharwal*, Sandeep Kumar Shukla*, Archna Pandey* Department of Chemistry Dr. Harisingh Gour Vishwavidyalaya (A Central University), Sagar (M.P) 470003 E-mail Id: rpaharwal9@gmail.com, prof.archnapandey@gmail.com In general, the development of drug nanocarriers for poorly soluble pharmaceuticals represents a special task and still faces some unresolved issues. The therapeutic application of hydrophobic, poorly water-soluble agents is associated with some serious problems, since low water-solubility results in poor absorption and low bioavailability [1]. In addition, drug aggregation upon intravenous administration of poorly soluble drugs might lead to such complications as embolism [2] and local toxicity [3]. On the other hand, the hydrophobicity and low solubility in water appear to be intrinsic properties of many drugs [4], since it helps a drug molecule to penetrate a cell membrane and reach important intracellular targets [5-6]. To overcome the poor solubility of certain drugs, clinically acceptable organic solvents are used in their formulations, as well as liposomes [7]. Another alternative is associated with the use of various micelle-forming surfactants in formulations of insoluble drugs. Delivering water-insoluble drugs, reducing severe systemic toxicities and increasing the utilization of drugs by improving their pharmacokinetics posed many challenges for drug delivery system (DDS) and drug development [8]. Recently, several type of drug carrier, such as microspheres, liposomes, nanoparticles [9] and polymeric carriers have been investigated as DDS, but non-selective scavenging of these carriers by the reticuloendothelial system (RES) is a serious problem. The structure of nanocarriers first developed in 1970 has since been optimized in order to improve their biodistribution. Polyethylene glycol (PEG) is grafted to nanoparticles and liposomes, increasing their stealth capacity and consequently, their systemic residence time in the blood-stream [10]. Nanocarriers injected by the systemic route and used for drug delivery have to respect some essential conditions [11]. 1 ISBN : 978-93-85073-24-3 As new polymers with innovative properties became available, selection of the right polymers for certain application became critically important. This led to strong demands on more efficient and more functional drug delivery vehicles. As polymers with new properties were developed, more needs were founds to develop polymers with even more intricate properties. It is most desirable if the polymers with advanced properties are synthesized with specific functions designed for drug delivery, such as drug solubilization and drug targeting, and for solving emerging problems. For this reason, it is beneficial to understand the current drug delivery technologies and the unique roles of polymers [12]. The various forms of natural and synthetic polymers are used for drug encapsulation and to deliver compound. The chitosan, a natural and antioxidative polymer obtained from crustacean shell [13] and the synthetic polymers L-D-, and D,L- polylactic acid (PLA), polyglycolic acid (PGA), and polycaprolactic acid (PCL), polyvinyl alcohol, polyethylene glycol (PEG), poly-Nvinylpyrrolidone, etc. have been used for controlled release of drug thereby reducing unwanted side effects and improving therapy [14-17]. The formulation of nano-sized particles can be implemented to all drug compounds belonging to biopharmaceutical classification system (BCS) classes II and IV to increase their solubility and hence partition into gastrointestinal barrier [18]. Micronization is used for class II drugs of (BCS), i.e. drugs having a good permeability and poor solubility [19-21]. There are many conventional methods for increasing the solubility of poorly soluble drugs, which include micronization [22], solubilisation using co-solvents [23], salt form [24], surfactant dispersions [25], precipitation technique [26-27] and oily solution. Other techniques are like liposomes [28], emulsions [29-30], microemulsion [31-32], solid dispersion [33-34], and inclusion complexation using cyclodextrins [35-37] show sensible achiever, but they lack in universal applicability to all drugs. These techniques are not applicable for those drugs which are not soluble in aqueous and organic solvents. Nanotechnology can be used to solve the problems associated with these conventional approaches for solubility and bioavailability enhancement. Nanotechnology is a multidisciplinary scientific field undergoing explosive development. One of the greatest values/promises of nanotechnology is in the development of new and effective medical treatments, such as nanomedicine [38-39]. The early concept of nanomedicine was inspired from the idea of fabrication of nano2

ISBN : 978-93-85073-24-3 sized robots that could be introduced into the human body and perform cellular repairs at the molecular level. However, the first scientist to voice the possibility of nanomedicine was the Noble physicist Richard P. Feymman, who in his historical 1959 lecture at Caltech said “There’s plenty of room at the bottom” and even proposed the first known nanomedical procedure to cure heart disease through “swallowing the surgeon” [40]. Based on Feymman’s insight into nanomedicine, manipulation of the materials/devices at the molecular level, nanomedicine today has evolved and branched into hundreds of different directions, e.g. nanoparticles, biosensors and nanotherapeutics [41]. Many approaches are being actively pursued towards nanomedicine. One of the nanomedical approache is to develop nanoparticles as carriers for drug molecules to achieve enhanced bioavailability, therefore, controlled drug delivery [42-44]. Bioavailability refers to the availability of the drug molecules at the specific site over a period of time. Tuning for enhanced bioavailability, which could not be achieved by the small-molecule drug alone, relies on manipulations of the nanocarriers. Nanoparticles (diameter < 1000 nm) are one attractive system as nanocarriers for controlled drug delivery, because they have the abilities to protect the therapeutics from degradation and help them to achieve improved solubility, increased loading capacity, and prolonged circulation time. In addition, they can also incorporate multiple types of therapeutics and various detection elements into one single formulation for imaging and more effective treatment. Furthermore, they can be functionalized with targeting ligands at the surface to obtain targeted delivery of therapeutics [45-54]. Therefore, lots of researchers are pursuing the nanoparticle approach for therapeutic delivery. The arsenal of nanoparticles at the forefront of the controlled drug delivery research includes dendrimers [55-57], polymer micelles [5860], chitosan nanoparticles [61-65], liposomes [66-70], polymersomes [71-76], carbon assemblies [77-79], gold nanoparticles including nanoshells and nanocages [80-84]. Need of Nanocarriers Nanotechnology is a novel area of science that provides, with a new hope, the tools and technology to work at atomic, molecular and supramolecular levels leading to creation of devices and delivery systems with fundamentally new properties and 3 ISBN : 978-93-85073-24-3 functions. A nanocarrier offers a number of advantages making it an ideal drug delivery vehicle (Fig 1.1). • Nanocarriers can better deliver drugs to tiny areas within the body [85]. • It represents engineering of particles, which are smaller than 100 nanometers. • Nanotechnology is so complementary to biotechnology that promises to bridge the gaps between ‘the structure’ and ‘the function’ of biomolecules as well as between ‘human physiology’ and ‘pathophysiology’. • This allows the engineering of products on a comparable scale to nature such as biologicals like proteins, DNA and viruses, which are of the order of 10’s of nanometers in size and cells and cellular assemblies of the order of 1000’s of nanometers. • It involves overlap of biotech, nanotech, and information technology, might result in many important applications in life sciences including areas of gene therapy, drug delivery, imaging, biomarkers, biosensors and novel drug discovery techniques [8688]. • It also offers an attractive solution for transformation of biosystems, and provides a broad platform in several areas of bioscience [89-90]. • Nanocarriers overcome the resistance offered by the physiological barriers in the body because efficient delivery of drug to various parts of the body is directly affected by particle size. • Nanocarriers aid in efficient drug delivery to improve aqueous solubility of poorly soluble drugs [91-92], that enhance bioavailability [93] for timed release of drug molecules, and precise drug targeting [94-95]. • The surface properties of nanocarriers can be modified for targeted drug delivery [96-97] e.g. small molecules, proteins, peptides, and nucleic acids loaded nanoparticles are not recognized by immune system and efficiently targeted to particular tissue types [98]. • Targeted nano drug carriers reduce drug toxicity and provide more efficient drug distribution [99]. 4

ISBN : 978-93-85073-24-3 • Nanocarriers holds promise to deliver biotech drugs over various anatomic extremities of body such as blood brain barrier, branching pathways of the pulmonary system and the tight epithelia junctions of the skin etc. • Nanocarriers better penetrate tumors due to their leaky constitution, containing pores ranging from 100-1000 nm in diameter. Fig 1.1: Multidisciplinary Functions of Nanocarriers Anti-Depressants Depression is estimated to affect nearly 340 million people worldwide and 18 million people in the United States at any given time [100]. A number of studies have documented the enormous impact of this debilitating condition on both patients and the health care system [101-104]. In the primary care setting, diagnosis of a depressive disorder is complicated by the fact that depressed patients frequently present with a combination of emotional and physical symptoms [105-109]. The importance of physical symptoms was highlighted by a recent international study which found that almost 70% of depressed patients reported physical symptoms as the only reason for visiting their physician [110]. Physical symptoms often associated with depression include headaches, back pain, gastrointestinal disturbance (e.g., irritable bowel syndrome), and generalized aches and pains [111]. 5 ISBN : 978-93-85073-24-3 Antidepressants are usually classified according to structure [e.g., tricyclic antidepressants (TCAs)] or function [e.g., monoamine oxidase inhibitors (MAOIs), selective serotonin reuptake inhibitors (SSRIs)]. However, it may be more useful to classify them according to the acute pharmacologic effects that are presumed to trigger behavioral improvement. If this is done, the antidepressants can be grouped in four categories (Table 1.1). First are the drugs that selectively block the reuptake of norepinephrine (NE). These include certain TCAs and TCA-like compounds (maprotiline). Another drug that falls into this category is reboxetine, although it is distinct structurally from the TCAs and TCA like compounds [112]. Second are the SSRIs, which, as their class name implies, selectively block the reuptake of serotonin [5-hydroxytryptimine (5-HT)] in-vivo. Third are the drugs that act nonselectively on noradrenergic and serotoninergic neurons with a resultant enhancement of synaptic transmission. Some TCAs are in this category, as are the MAOIs. Some novel drugs are also in this category [113]. Table 1.1: MECHANISM-BASED CLASSIFICATION FOR ANTIDEPRESSANTS Category Mechanism I Selective blockade of NE reuptake (SNRIs) II Selective blockade of 5- HT reuptake (SSRIs) Nonselective III enhancement of NE and 5-HT transmission Unknown potent IV stimulatory effects on NE or 5-HT Examples DMI, NT amoxapine, maprotiline reboxetine Citalopram, fluoxetine, paroxetine, sertraline IMI, AMI phenelzine, tranylcypromine venlafaxine, mirtazapine Trimipramine, bupropion, nefazodone, trazodone TCA Current Classification (If Any) TCAs , TCA-like SSRIs TCAs, MAOIs (sometimes with SSRIs) 6

ISBN : 978-93-85073-24-3 5-HT, 5-hydroxytryptamine (serotonin); AMI, amitriptyline; DMI, desipramine; IMI, imipramine; MAOI, monoamine oxidase inhibitor; NE, norepinephrine; NT, nortriptyline; SNRI, selective norepinephrine reuptake inhibitor; SSRI, selective serotonin reuptake inhibitor; TCA, tricyclic antidepressant. In the fourth and final heterogeneous group are drugs without known potent, acute pharmacologic effects that result in enhancement of noradrenergic or serotoninergic transmission. In other words, their mechanisms of action are unknown. Drugs in this category include the TCA trimipramine and also bupropion, nefazodone, and trazodone. It has been speculated that bupropion acts through dopaminergic mechanisms because it is the only antidepressant that more potently blocks the reuptake of dopamine than that of either NE or 5-HT [114]. The brain is one of the most important organs of the human body, if not the most, and its homeostasis is of primary importance. In fact, specific interfaces also referred to as barriers; tightly regulate the exchange between the peripheral blood circulation and the cerebrospinal fluid (CSF) circulatory system. These barriers are represented by the choroids plexus (CP) epithelium (blood-ventricular CSF), the arachnoid epithelium (blood-subarachnoid CSF), and the BBB (blood-brain barrier interstitial fluid) [115]. The blood-brain barrier (BBB) (Fig 1.2) is the bottleneck in brain drug development and is the single most important factor limiting the future growth of neurotherapeutics [116]. 7 ISBN : 978-93-85073-24-3 Fig 1.2: Whole body autoradiogram of an adult mouse sacrificed 30 min after intravenous injection of radiolabeled histamine, a small molecule that readily enters all organs of the body, except for the brain and spinal cord. The transport of small molecules across the BBB is the exception rather than the rule, and 98% of all small molecules do not cross the BBB [117]. Despite the large number of patients with disorders of the CNS and despite the fact that so few large or small molecule therapeutics cross the BBB, there are few pharmaceutical companies in the world today that have built a BBB drug targeting program. However, even if a pharmaceutical company decided to develop a BBB program, there would be few BBB-trained scientists to hire because less than 1% of U. S. academic neuroscience programs emphasize BBB transport biology. Because most drugs do not cross the BBB, and because the industry is not providing solutions to the BBB problem, it is not surprising that most disorders of the CNS could benefit from improved drug therapy (Fig 1.3). For a small molecule drug to cross the BBB in pharmacologically significant amounts, the molecule must have the dual molecular characteristics of a) molecular mass under a 400- to 500-Da threshold, and b) high lipid solubility. 8

ISBN : 978-93-85073-24-3 Fig 1.3: Comprehensive Medicinal Chemistry database shows that, of more than 7000 small-molecule drugs, only 5% treat the CNS, and these drugs only treat four disorders: depression, schizophrenia, chronic pain, and epilepsy. There are few effective small or large molecule drugs for the majority of CNS disorders, with the exception of Parkinson’s disease, e.g., L-DOPA, and multiple sclerosis, e.g., cytokines. Solubility and Dissolution The term ‘solubility’ is defined as maximum amount of solute that can be dissolved in a given amount of solvent. It can also be defined quantitatively as well as qualitatively. Quantitatively it is defined as the concentration of the solute in a saturated solution at a certain temperature. In qualitative terms, solubility may be defined as the spontaneous interaction of two or more substances to form a homogenous molecular dispersion. A saturated solution is one in which the solute is in equilibrium with the solvent [118-120]. The solubility behavior of drugs remains one of the most challenging aspects in formulation development. The events that occur following oral administration of a solid dosage form. It is a well formulated dosage form; the two critical rate determining steps in the absorption of orally administered drugs are [121]: 9 ISBN : 978-93-85073-24-3 1. Rate of dissolution 2. Rate of drug permeation through the biomembrane. To assist successful oral drug development, in vitro dissolution testing has emerged as a preferred method to evaluate development potential of new APIs and drug formulations are shown in Fig 1.4. Fig 1.4: Roles of in-vitro dissolution testing in pharmaceutical drug development The Nernst-Brunner and Levich modification of the Noyes-Whitney equation (eq.1) identified the important factors to the kinetics of in-vivo drug dissolution. These factors include drug diffusivity and solubility in the GI contents, the surface area of the solid wetted by the luminal fluids and the GI hydrodynamics [122]. ------------------ 1 Where dC/dt is the dissolution rate, A is the surface area available for dissolution, D is the diffusion coefficient of the drug, Cs is the saturation solubility of the drug in the dissolution medium, C is the concentration of drug in the medium at time (t) and h is the thickness of the diffusion boundary layer adjacent to the surface of dissolving drug. Several physicochemical and physiological aspects can have a great influence on the factors in eq. (1) and therefore on the dissolution rate, such as crystalline form, 10

ISBN : 978-93-85073-24-3 drug lipophilicity, particle size, viscosity of the medium, solubilization by native surfactants and co-ingested foodstuffs and pka in relation to the GI pH profile [123]. The mechanism and kinetics of drug release are dependent on the solubility of the active moiety and the swelling and erosion properties of the polymer, with water soluble drugs being released predominantly by diffusion with a limited contribution from matrix erosion and anomalous diffusion resulting from the relaxation of the macromolecular polymer chains [124]. The release of water soluble moieties will typically follow first order release kinetics. Water insoluble drugs are released predominantly through matrix erosion and therefore exhibit time independent or zeroorder release kinetics [125-130]. The Biopharmaceutics Classification System In the past decade, a greater understanding of the molecular transport in relation to physico-chemical properties especially solubility has led to the evolution of a biopharmaceutics classification system (BCS), which is becoming a road map governing future drug design, development and delivery. The BCS sets the criteria for allowing a drug substance, in an immediate release form to circumvent a bioequivalence study. It classifies the drugs into four major categories (Table 1.2) according to two main parameters; the solubility and permeability behaviours of each molecule [131-132]. Table 1.2: Biopharmaceutics Classification System (BCS) of drug molecules Biopharmaceutics Classification System I High solubility – High permeabilitya II Low solubility – High permeabilityb III High solubility – Low permeabilityc IV Low solubility – Low permeabilityd a. Exhibit dissolution rate-limited absorption (generally very well absorbed). b. Exhibit solubility rate-limited absorption. c. Exhibit permeability rate-limited absorption. d. Exhibit both, solubility and permeability rate-limited absorption with very poor oral bioavailability. 11 ISBN : 978-93-85073-24-3 According to the BCS, the dissolution rate is the limiting factor for the absorption of class II and IV drugs. Currently, 40% of the NCE fall in these two classes. Such molecules provide potential challenges to the formulation scientist. Their poor water solubility almost inevitably leads to low oral bioavailability from conventional dose forms. Poor aqueous solubility is an industry wide issue, especially for pharmaceutical scientists in drug discovery and drug development. A poorly water soluble drug is usually associated with poor absorption and bioavailability upon oral administration [133]. Although a certain degree of hydrophobicity is necessary for a drug molecule to cross the cell membrane easily [134], the overall rate of absorption is dictated by the time required for the dosage form to release its contents, and for the drug to dissolve in the GI fluid [135]. The water solubility of ‘poorly soluble’ drugs is usually less than 100 µg/mL [136]. A further parameter useful for identifying ‘poorly soluble’ drugs is the dose:solubility ratio of the drug. The dose:solubility ratio is defined as the volume of GI fluids necessary to dissolve the administered dose. When this volume exceeds the volume of fluids available, one may anticipate incomplete bioavailability from solid oral dosage forms. In fact, developing dissolution test methods for poorly water-soluble drug products has been an important task to formulation scientists. Problems encountered with poorly water-soluble drug product include a low extent of drug release and a slow release rate. General strategies to enhance their dissolution patterns rely upon either changing the dissolution medium pH, or adding solubilizers such as surfactants cyclodextrin derivatives into a dissolution medium [137-145]. Polymeric Surfactant Detergents belongs to a class of compounds called surfactants, which are surface active agents that reduce interfacial surface tension in mixture (i.e., oil and water) by adsorbing to interfaces [146]. The ability of a detergent to participate in a specific biological/biochemical function is related to its structure; the polar hydrophilic portion of the detergents molecule is referred to as the “hydrophilic head group” while the nonpolar hydrophobic, portion is referred to as the “tail”. Surfactants play a major role in the absorption of drugs in the body [147-148]. In the late 1960s, micelles drew much 12

ISBN : 978-93-85073-24-3 attention as drug carriers owing to their easily controlled properties and good pharmacological characteristics [149-150]. Fig 1.5: Schematic representation of the micellization process. Micelles are formed when amphiphiles are placed in water. They consist of an inner core of assembled hydrophobic segments capable of solubilizing lipophilic substances and outer hydrophilic corona serving as a stabilizing interface between the hydrophobic core and the external aqueous environment [151]. Fig 1.6: Schematic representation of the micelle formation 13 ISBN : 978-93-85073-24-3 Micellization is a critical phenomenon when considering detergent applications. Each detergents can be characterized by its critical micelle concentration (CMC); the concentration of detergents above which monomers self-assemble into non-covalent aggregates (called micelles) [152-153]. The CMC actually does not occur at a single concentration, but rather, over a narrow concentration range. When the total detergent concentration is below the CMC, detergent monomers are free in bulk solution. However, as more detergent is added above the CMC, all additional detergent monomers will go into micelles. It is important to note that when the total detergents concentration is greater than the CMC, there is a monomeric detergent concentration equal to the CMC and a micellar detergent concentration equal to: (total detergent concentration)-CMC. The CMC can be determined by a variety of methods including surface tension measurements and conductivity measurements [154-155]. Micelles made of nonionic surfactants are widely used as adjuvants and drug carrier system in many areas of pharmaceutical technology and controlled drug delivery [156-161]. Significance of works In the last decade, an emerging interest has been growing towards brain drug targeting where issues have been widely discussed [162-169]. The increasing awareness of the lack of rational and common efforts among different and complementary research areas has pointed out the need for a deeper understanding and a closer collaboration among diverse research experts of the field [170]. The use of nanotechnology in medicine and more specifically drug delivery is set to spread rapidly. For decades pharmaceutical sciences have been using nanoparticles to reduce toxicity and side effects of drugs. The book has explored the most recent intellectual property on polymer-based nanotechnological strategies for the design of optimal drug delivery devices. This is an effervescing area, with large investment volumes being poured by both public and private sectors. Most of the novelties still remain as experimental enterprises and are dealing with regulatory agencies to get approval. A key feature is to understand and internalize that due to the multidisciplinary nature of this area convergence of different professionals is demanded to bring a product to the market. In this book, we found that calculation and comparison of partial solubility parameters of polymer and drug could be used as a reliable means to predict which 14

ISBN : 978-93-85073-24-3 polymer is most suitable for development of a micelle based formulation for a specific drug. The ultimate goal of the test is to generate information that can provide an insight into the mechanism by which the drug is being released from the dosage form and provide data to facilitate the rational and rapid research, optimization and development of a modified release dosage form. The drug releases based on polymeric micelles that are currently in clinical trial evaluation represent significant milestones in this area. These advancements encourage further efforts in the design and development of polymer based nanocarriers as drug delivery vehicles. The interdisciplinary efforts in this field bringing together polymer chemist, pharmaceutical and medical scientists will continue to move the research forward to create new and improved technologies that are tailor made to suit the delivery of challenging molecules. It is only a matter of time before a formulation based on polymeric micelles is approved for treatment. The technological approach is a non-invasive method of drug delivery to the CNS. It is based on the use of nanosystems (colloidal carriers), which could be lipidbased (liposomes or solid lipid nanoparticles) or polymer-based nanoparticles. Nanotechnological approaches to neurodevelopmental, neurological and neuropsychiatric disorders include (a) using nanoparticles or nanocarriers to deliver drug or gene therapies, (b) using nanotechnology to reconstruct, reinforce, and/or stabilize the cytoskeletal matrix, (c) using nanofabrication methods to make biohybrid transport devices, and (d) coating electrodes with nanoparticles. Thus, this field of research represents one of the most stimulating challenges for the scientific world, as a result of the limited number of therapeutics capable of reaching the most ‘secret and sacred’ system of the body, the CNS. Nanotechnology is a multi disciplinary field, convergence of basic sciences and applied disciplines like biophysics, molecular biology, and bio engineering. Size reduction is a fundamental unit operation having important application in pharmacy. Major advantages of nano sizing include - (a) Increase surface (b) Enhanced solubility (c) Increase rate of dissolution and oral bio availability (d) Rapid onset of action (e) Less amount of dose required in the field of pharmacy. For applications to medicine and physiology these materials and devices can be designed to interact with a 15 ISBN : 978-93-85073-24-3 high degree of functional specificity, thus allowing a degree of interaction between technology and biological systems not previously attainable. It should be appreciated that nanotechnology is not in itself a single emerging scientific discipline but rather a meeting of traditional sciences such as chemistry, physics material science and biology to bring together the required collective expertise needed to develop these noval technologies. Numerous nanoparticle-based drug delivery and drug targeting systems are currently developed or under development. Their use aims to minimize drug degradation upon administration, prevent undersirable side effects, and increase drug bioavailability and the fraction of the drug accumulated in the pathological area. Author wish that pharmaceutical drug carriers, especially the ones for parenteral administration, are expected to be easy and reasonably cheap to prepare, biodegradable, have small particle size, possess high loading capacity, demonstrate prolonged circulation, and, ideally, specifically or non-specifically accumulate in required pathological sites in the body. In conclusion, even a brief listing of some key problems of nanocarriermediated drug delivery to brain shows how broad and intense this area is. In addition to this, nanoscale-based delivery strategies are beginning to make a significant impact on BBB- targeting programe and also global pharmaceutical planning and marketing. References 1. V. P. Torchilin, J. Control. Rel., 73, 137, 2001 2. A. M. Fernandez, K. Van Derpoorten, L. Dasnois, K. Lebtahi, V. Dubois, T. J. Lobl, S. Gangwar, C. Oliyai, E. R. Lewis, D. Shochat, A. Trouet, J. Med. Chem., 44, 3750, 2001. 16

ISBN : 978-93-85073-24-3 3. 4. S. H. Yalkowsky, (eds.), Techniques of Solubilization of Drugs, Marcel Dekker, New York and Basel, 1981. B. A. Shabner, J. M. Collings, (eds.), Cancer Chemotherapy: Principles and Practice, J. B. Lippincott Co, Philadelphia, 1990. 5. K. Yokogawa, E. Nakashima, J. Ishizaki, H. Maeda, T. Nagano, F. Ichimura, Pharm. Res., 7, 691, 1990. 6. A. Hageluken, L. Grunbaum, B. Nurnberg, R. Harhammer, W. Schunack, R. Seifert, Biochem. Pharmacol., 47, 1789, 1994. 7. D. D. Lasic and Papahadjopoulos, (eds.), Medical Application of Liposomes, Elsevier, New York, 1998. 8. O. Praneet, N. Tanasait, C. Amornrut, R. Theerasak, A. Auayporn, Eur. J. Pharm. Sci., 30, 424-431, 2007. 9. S. H. Chang, A. Joseph, J. Gardella, Chem. Rev., 105, 4205-4232, 2005. 10. S. M. Moghimi, J. Szebeni, Prog. Lipid Res., 42, 463-78, 2003. 11. P. R. Lockman, R. J. Mumper, M. A. Khan, D. D. Allen, Drug Dev. Ind. Pharm., 28, 1-13, 2002. 12. S. Kim, J. H. Kim, O. Jeon, I. C. Kwon, K. Park, Eur. J. Pharm. Biopharm., 71, 420-430, 2009. 13. F. Shahidi, R. Abuzayton, Adv. Food Nutr. Res., 49, 93-137, 2005. 14. Y. Dong, S. S. Feng, Biomate., 25, 2843-2849, 2004. 15. S. Gupta, A. Pandey, Oxid. Comm., 30, 192-196, 2007. 16. R. Jain, A. Pandey, Bull. J. Chem. Edu., 18, 126-134, 2009. 17. N. Nema, A. Pandey, Oxid. Comm., 34, 98-107, 2011. 18. R. Dubey, Drug Deliv. Technol., 6, 65-67, 2006. 19. G. L. Amidon, H. Lennernas, V. P. Shah, J. R. Crison, Pharm. Res., 12, 413420, 1995. 20. L. X. Yu, G. L. Amidon, J. E. Polli, H. Zhao, M. U. Mehta, D. P. Conner, V. P. Shah, L. J. Lesko, M. L. Chen, V. H. Lee, A. S. Hussain, Pharm. Res., 19, 921925, 2002. 21. H. Lennernas, B. Abrajamsson, J. Pharm. Pharmacol., 57, 273-285, 2005. 17 ISBN : 978-93-85073-24-3 22. J. Varshosaz, R. Talari, S. A. Mostafavi, A. Nokhodchi, Powder Technol., 187, 222-230, 2008. 23. S. Pahala, M. A. Joan, H. Y. Samuel, Int. J. Pharm., 213, 25-29, 2001. 24. T. M. Abu Serajuddin, Adv. Drug Deliv. Rev., 59, 603-616, 2007. 25. S. M. Wong, I. W. Kellaway, S. Murdan, Int. J. Pharm., 317, 61-68, 2006. 26. S. Marazhon, B. Judith, C. Xiaoxia, S. Steve, R. O. Williams III, P. J. Keith, Int. J. Pharm., 243, 17-31, 2002. 27. L. R. True, B. G. Ian, E. H. James, L. F. Kevin, A. C. Clindy, J. T. Chritoper, Pharm. Res., 21, 2048-57, 2004. 28. M. Riaz, Pak. Pharm. Sci., 8, 69-79, 1995. 29. A. G. Floyd, Pharm. Sci. Tech., 4, 134-143, 1999. 30. M. Nakano, Adv. Drug Deliv. Rev., 45, 1-4, 2000. 31. K. R. Jadhav, I. M. Shaikh, K. W. Ambade, V. J. Kadam, Cur. Drug Deliv., 3, 267-273, 2006. 32. M. J. Lawrence, G. D. Rees, Adv. Drug Deliv. Rev., 45, 89-121, 2000. 33. C. Leuner, J. Dressman, Eur. J. Pharm. Biopharm., 50, 47-60, 2000. 34. N. J. Hemant, W. T. Ravindra, D. Martha, P. S. Vaishali, J. Mohammed, S. B. Mohinder, A. V. Sailesh, T. M. Abu Serajuddin, Int. J. Pharm., 269, 251-258, 2004. 35. V. J. Stella, R. A. Rajewski, Pharm. Res., 14, 556-567, 1997. 36. T. Loftsson, M. Brewster, J. Pharm. Sci., 85, 1017-1025, 1996. 37. L. Marcela, M. Maria de Bertorello, L. Marcela, Int. J. Pharm., 159, 13-18, 1997. 38. R. A. Freitas, Nanomedicine, Volume I: Basic capabilities. Georgetown, TX, 1999. 39. R. A. Freitas, Nanomedicine, Volume IIA: Biocompatibility. Georgetown, TX, 2003. 40. R. P. Feynman, Eng. Sci., (CalTech), 23, 22-36, 1960. 41. R. A. Freitas, Nanomed., 1, 2-9, 2005. 42. L. Y. Shargel, A. B., “Applied Biopharmaceutics and Pharmacokinetics” 4th McGraw-Hill, New York, 1999. 18 (ed.),

ISBN : 978-93-85073-24-3 43. K. V. Jaspreet, K. R. Maram, D. L. Vinod, Curr. Nanosci., 1, 47-64, 2005. 44. K. K. Jain, Drug Discov. Today, 10, 1435-1442, 2005. 45. M. D. Chavanpatil, A. Khdair, J. Panyam, J. Nanosci. Nanotechnol., 6, 2651-2663, 2006. 46. P. Couvreur, F. Puisieux, Adv. Drug Deliv. Rev., 10, 141-162, 1993. 47. P. Couvreur, C. Vauthier, Pharm. Res., 23, 1417-1450, 2006. 48. M. E. Davis, Z. Chen, D. M. Shin, Nat. Rev. Drug Discov., 7, 771-782, 2008. 49. D. F. Emerich, C. G. Thanos, J. Drug Targ., 15, 163-183, 2007. 50. D. A. Groneberg, M. Giersig, T. Welte, U. Pison, Curr. Drug Targ., 7, 643-648, 2006. 51. P. R. Lockman, R. J. Mumper, M. A. Khan, D. D. Allen, Drug Dev. Ind. Pharm., 28, 1-13, 2002. 52. C. Medina, M. J. Santos-Martinez, A. Radomski, O. I. Corrigan, M. W. Radomski, Br. J. Pharmacol., 150, 552-558, 2007, 53. M. E. Napier, J. M. Desimone, Polym. Rev., 47, 321-327, 2007. 54. M. Rawat, D. Singh, S. Saraf, Biol. Pharm. Bull., 29, 1790-1798, 2006. 55. J. B. Wolinsky, M. W. Grinstaff, Adv. Drug Deliv. Rev., 60, 1037-1055, 2008. 56. N. Malik, J. Control. Rel., 65, 133-148, 2000. 57. E. R. Gillies, J. M. J. Frechet, Drug Discov. Today, 10, 35-43, 2005. 58. G. Gaucher, M. H. Dufresne, V. P. Sant, N. Kang, D. Maysinger, J. C. Leroux, J. Control. Rel., 109, 169-188, 2005. 59. K. Kataoka, A. Harada, Y. Nagasaki, Adv. Drug Deliv. Rev., 47, 113-131, 2001. 60. V. P. Torchilin, Pharm. Res., 24, 1-16, 2007. 61. N. Bhattarai, H. R. Ramay, J. Gunn, F. A. Matsen, M. Q. Zhang, J. Control. Rel., 103, 609-624, 2005. 62. J. M. Dang, K. W. Leong, Adv. Drug Deliv. Rev., 58, 487-499, 2006. 63. N. A. Peppas, J. Z. Hilt, A. Khademhosseini, R. Langer, Adv. Mater., 18, 1345-1360. 2006, 64. M. Prabaharan, J. F. Mano, Drug Deliv., 12, 41-57, 2005. 19 ISBN : 978-93-85073-24-3 65. K. Roy, H. Q. Mao, S. K. Huang, K. W. Leong, Nat. Med., 5, 387-391, 1999. 66. T. L. Andresen, S. S. Jensen, K. Jorgensen, Prog. Lipid Res., 44, 68-97, 2005. 67. J. Y. Fang, T. L. Hwang, Y. L. Huang, Curr. Nanosci., 2, 55-70, 2006. 68. M. Hashida, S. Kawakami, F. Yamashita, Chem. Pharm. Bull., 53, 871-880, 2005. 69. M. L. Immordino, F. Dosio, L. Cattel, Int. J. Nanomed., 1, 297-315, 2006. 70. M. Voinea, M. Simionescu, J. Cell. Mol. Med., 6, 465-474, 2002. 71. D. A. Christian, S. Cai, D. M. Bowen, Y. Kim, J. D. Pajerowski, D. E. Discher, Eur. J. Pharm. Biopharm., 71, 463-474, 2009. 72. D. E. Discher, V. Ortiz, G. Srinivas, M. L. Klein, Y. Kim, C. A. David,S. S. Cai, P. Photos, F. Ahmed, Prog. Polym. Sci., 32, 838-857, 2007. 73. C. LoPresti, H. Lomas, M. Massignani, T. Smart, G. Battaglia, J. Mater. Chem., 19, 3576-3590, 2009. 74. F. H. Meng, Z. Y. Zhong, J. Feijen, Biomacromole., 10, 197-209, 2009. 75. Z. Q. Pang, W. Lu, H. L. Gao, K. L. Hu, J. Chen, C. L. Zhang, X. L. Gao, X. G. Jiang, C. Q. Zhu, J. Control. Rel., 128, 120-127, 2008. 76. J. P. Xu, J. Ji, W. D. Chen, J. C. Shen, J. Control. Rel., 107, 502-512, 2005. 77. N. Venkatesan, J. Yoshimitsu, Y. Ito, N. Shibata, K. Takada, Biomat., 26, 7154-7163, 2005. 78. A. Bianco, K. Kostarelos, M. Prato, Curr. Opin. Chem. Biol., 9, 674-679, 2005. 79. A. Bianco, J. Hoebeke, S. Godefroy, O. Chaloin, D. Pantarotto, J. P. Briand, S. Muller, M. Prato, C. D. Partidos, J. Am. Chem. Soc., 127, 58-59, 2005. 80. L. R. Hirsch, A. M. Gobin, A. R. Lowery, F. Tam, R. A. Drezek, N. J. Halas, J. L. West, Ann. Biomed. Eng., 34, 15-22, 2006. 81. R. Padmavathy, M. Hariharan, Res. J. Biotechnol., 117-122, 2008. 82. S. R. Sershen, S. L. Westcott, N. J. Halas, J. L. West, J. Biomed. Mate. Res., 51, 293-298, 2000. 83. A. S. Zahr, M. de Villiers, W. V. Pishko, Langmu., 21, 403-410, 2005. 84. S. Kwang Hyun, K. Chulhong, C. M. Cobley, X. Younan, L. V. Wang, Proceedings of the SPIE, 7177, 71772-71779, 2009. 20

ISBN : 978-93-85073-24-3 85. M. C. Roco, R. S. Williams, P. Alivisatos, (eds.), Biological, medical and health applications. “Nanotechnology Research Directions,” Chap. 8, Kluwer Academic Publishers, Boston, 2000. 86. M. C. Roco, Curr. Opin. Biotechnol., 14, 337-343, 2003. 87. K. S. Sahoo, V. Labhasetwar, Drug Discov. Today, 8, 1112-1120, 2003. 88. J. M. Wilkinson, Med. Device Technol., 14, 29-31, 2003. 89. K. Bugunia-Kubik, M. Susisaga, J. Biosyst., 65, 123-138, 2002. 90. 91. J. Schimidt, C. Montemagno, Drug Discov. Today, 7, 500-503, 2002. J. E. Kipp, Int. J. Pharm., 284, 109-122, 2004. 92. L. Ould-ouali, M. Noppe, X. Langlois, B. Willems, P. T. Riele, P. Timmerman, M. E. Brewster, A. Arien, V. Preat, J. Control. Rel., 102, 657-668, 2005. 93. P. Arbos, O. Campaner, A. Miguel, A. Arangoa Miguel, J. M. Irache, J. Control. Rel., 96, 55-65, 2004. 94. C. H. Dubin, Mech. Enz. Nanotechnol., 126, 10-12, 2004. 95. H. Yamamoto, Y. Kuno, S. Sugimoto, H. Takeuchi, Y. Kawashima, J. Control. Rel., 102, 373-381, 2005. 96. L. B. Peppas, J. O. Blanchette, Adv. Drug Deliv. Rev., 56, 1649-1659, 2004. 97. J. Kreuter, Adv. Drug Deliv. Rev., 47, 65-81, 2001. 98. H. Cohen, R. J. Levy, J. Gao, I. Fishbein, V. Kousaev, S. Sosnowski, S. Slomkowski, G. Golomb, Gene Ther., 7, 1896-1905, 2000. 99. R. Lobenberg, J. Kreuter, AIDS Res. Hum. Retroviruses, 12, 1709-1715, 1996. 100. C. J. L. Murray, A. D. Lopez, (eds.), "The Global Burden of Disease" Cambridge, Mass: Harvard University Press; 1996. 101. P. E. Greenberg, L. E. Stiglin, S. N. Finkelstein, J. Clin. Psychiatry., 54, 405– 419, 1993. 102. D. L. Musselman, D. L. Evans, C. B. Nemeroff, Arch. Gen. Psychiatry., 55, 580–592, 1998. 103. H. A. Pincus, A. R. Pettit, J. Clin. Psychiatry., 62, 5–9, 2001. 104. R. Schulz, S. R. Beach, D. G. Ives, Arch. Int. Med., 160, 1761–1768, 2000. 105. D. P. Goldberg, K. Bridges, J. Psychosom. Res., 32, 137–144, 1988. 21 ISBN : 978-93-85073-24-3 106. L. J. Kirmayer, J. M. Robbins, M. Dworkind, Am. J. Psychiatry., 150, 734– 741, 1993. 107. P. D. Gerber, J. E. Barrett, J. A. Barrett, J. Gen. Intern. Med., 7, 170–173, 1992. 108. M. Posse, T. Hallstrom, Acta. Psychiatr. Scand., 98, 187–192, 1998. 109. K. Barkow, R. Heun, T. B. Ustun, Eur. Arch. Psychiatry. Clin. Neurosci., 251, 21–26, 2001. 110. G. E. Simon, M. Von Korff, M. Piccinelli, N. Engl. J. Med., 341, 1329–1335, 1999. 111. K. Kroenke, R. K. Price, Arch. Intern. Med., 153, 2474–2480, 1993. 112. E. H. F. Wong, M. S. Sonders, S. G. Amara, Biol. Psychiat., 47, 818-829, 2000. 113. T. DeBoer, G. Maura, M. Raiteri, Neuropharmacol., 27, 399-408, 1988. 114. J. A. Ascher, J. O. Cole, J. N Colin, J. Clin. Psychiat., 56, 395-401, 1995. 115. M. B. Segal, Cell. Mol. Neurobiol., 20, 183-196, 2000. 116. W. M. Pardridge, "Brain Drug Targeting, The future of Brain Drug Development" Cambridge, U. K., Cambridge University Press, 2001. 117. W. M. Pardridge, Mol. Interv., 3, 90-105, 2003. 118. A. Martin, P. Bustamanate, A. H. C. Chun, “Physical Pharmacy” B. I. Wavely Pvt. Ltd, New Delhi, 4, 223, 1994. 119. A. Osol, (eds.) in: “Remington’s Pharmaceutical Sciences” Mack Publishing Company, Eastern Pennsylvania, 18, 203, 1990. 120. C. Neuberg, Biochem. J. Pharm., 75, 577, 1989. 121. D. Jennifer, K. Johannes, Pharmaceutical Dissolution Testing; Taylor & Francis, Boca Raton, FL, 2005. 122. J. B. Dressman, G. L. Amidon, C. Reppas, V. P. Shah, Pharm. Res., 15, 11-22, 1998. 123. D. Horter, J. B. Dressman, Adv. Drug Deliv. Rev., 46, 75-87, 2001. 124. C. D. Melia, Crit. Rev. Ther. Drug, 8, 395-421, 1990. 125. H. Lapidus, N. G. Lordi, J. Pharm. Sci., 57, 1292-1301, 1968. 22

ISBN : 978-93-85073-24-3 126. K. V. Ranga Rao, K. Padmalatha Devi, P. Buri, J. Control. Rel., 12, 133-141, 1990. 127. J. L. Ford, M. H. Rubinstein, F. McCaul, J. E. Hogan, P. J. Edgar, Int. J. Pharm., 40, 223-234, 1987. 128. M. J. Vazquez, J. L. Perez-Marcos, R. Gomez-Amoza, R. Martinez-Pacheco, C. Souto, A. Concheiro, Drug Dev. Ind. Pharm., 18, 1355-1375, 1992. 129. P. Colombo, R. Bettini, P. Santi, A. De Ascentis, N. A. Peppas, J. control. Rel., 39, 213-237, 1996. 130. P. Colombo, R. Bettini, N. A. Peppas, J. Control. Rel., 61, 83-91, 1999. 131. R. Lobenberg, G. L. Amidon, Eur. J. Pharm. Biopharm., 50, 3-12, 2000. 132. Y. H. Zhao, M. H. Abraham, J. Le, A. Hersey, C. N. Luscombe, G. Beck, B. Sherborne, I. Cooper, Pharm. Res., 19, 1446-1457, 2002. 133. C. A. Lipinski, F. Lombardo, B. W. Dominy, P. J. Feeney, Adv. Drug Deliv. Rev., 46, 3-26, 2001. 134. W. N. Charman, V. J. Stella, Adv. Drug Deliv. Rev., 7, 1-14, 1991. 135. C. A. Lipinski, J. Pharmacol. Toxicol. Meth., 44, 235-249, 2000. 136. A. Fasano, Trends Biotechnol., 16, 152-157, 1998. 137. L. R. Chen, J. A. Wesley, S. Bgattachar, B. Ruiz, K. Bahash, S. R. Babu, Pharm. Res., 20, 797-801, 2003. 138. S. Qazi, N. K. Peter Samuel, T. K. Venkatachalam, F. M. Uckun, Int. J. Pharm., 252, 27-39, 2003. 139. W. Sun, C. K. Larive, M. Z. Southard, J. Pharm. Sci., 92, 424-435, 2003. 140. A. C. Jain, M. C. Adeyeye, Int. J. Pharm., 212, 177-186, 2001. 141. J. A. Castillo, J. Palomo-Canales, J. J. Garcia, J. I. Lasters, F. Bolas, J. J. Torrado, Drug. Dev. Ind. Pharm., 25, 1241-1248, 1999. 142. J. R. Crison, N. D. Weiner, G. L. Amidon, J. Pharm. Sci., 86, 384-388, 1997. 143. V. P. Shah, J. J. Konecny, R. L. Everett, B. McCullough, A. C. Noorizadeh, J. P. Skelly, Pharm. Res., 6, 612-618, 1989. 144. J. G. Aunins, M. Southard, R. A. Meyers, K. J. Hemmeistein, V. J. Stella, J. Pharm. Sci., 74, 1305-1316, 1985. 145. A. I. Serajuddin, M. Rosoff, J. Pharm. Sci., 73, 1203-1208, 1984. 23 ISBN : 978-93-85073-24-3 146. M. Rosen, Surfactant and Interfacial Phenomena 3rd ed. Hoboken, John wiley & Sons, Inc., 2004. 147. J. Y. Wang, T. F. Yeh, Y. C. Lin, K. Miyamura, U. Holmskov, K. B. Reid, Thorax, 51, 907-13, 1996. 148. D. L. Carthew, G. Buckton , G. E. Parsons S. Poole, Pharm. Res., 13, 17301733, 1996. 149. M. Yokoyama, G. S. Kwon, T. Okano, Y. Sakurai, T. Seto, K. Kataoka, Bioconjugate Chem., 3, 295-301, 1992. 150. T. S. wiedmann, L. Kamel, J. Pharm. Sci., 91, 1743-1764, 2002. 151. K. Kataoka, G. S. Kwon, M. Yokoyama, T. Okano, Y. Sakurai, J. Control. Rel., 24, 119-132, 1993. 152. A. Helenius, Meth. Enzym., 56, 734-749, 1979. 153. P. Mukerjee, K. J. Mysels, Critical Micelle Concentrations of Aqueous Surfactant Systems , Vol. NSRDS-NBS 36, Washington, DC, National Beureau of Standards, 1970. 154. K. Stahler, J. Selb, P. Barthelemy, B. Pucci, F. Candau, Langmuir., 14, 4765, 1998. 155. K. Stahler, J. Selb, F. Candau, Coll. Poly. Sci. 276, 860, 1998. 156. S. N. Malik, D. H. Canaham, M. W. Gouda, J. Pharm. Sci., 64, 987-990, 1975. 157. K. Takada, H. Yoshimura, N. Shibata, Y. Masuda, H. Yoshikawa, S. Muranishi, T. Yasumura, T. Oka, J. Pharmacobio-Dyn., 9, 156-160, 1986. 158. D. D. Lasic, Nat., 355, 279-280, 1992. 159. G. Schubiger, J. Gruter, M. J. Shearer, J. Pediatr. Gastroenterol. Nutr., 24, 280284, 1997. 160. P. A. Redondo, A. I. Alvarez, J. L. Garcia, C. Villaverde, J. G. Prieto, Biopharm. Drug Dispos., 19, 65-70, 1998. 161. H. A. Bardelmeijer, M. Ouwehand, M. M. Malingre, J. H. Schellens, J. H. Beijnen, O. Van Tellingen, Can. Chemother. Pharmacol., 49, 119-125, 2002. 162. W. M. Pardridge, Drug Discov. Today, 7, 5-7, 2002. 163. R. N. Lawrence, Drug Discov. Today, 7, 223-226, 2002. 164. R. N. Lawrence, Drug Discov. Today, 7, 645-648, 2002. 24

ISBN : 978-93-85073-24-3 165. N. J. Abbott, Neurochem. Int., 45, 545-552, 2004. 166. J. F. Ghersi-Egea, Y. Sugiyama, Adv. Drug Deliv. Rev., 56, 1693-1694, 2004. 167. W. M. Pardridge, Cambridge University Press, Cambridge, UK, 2001. 168. M. Ricci, P. Blassi, S. Giovagnoli, C. Rossi, Curr. Med. Chem., 13, 1757-1775, 2006. 169. Y. Su, P. J. Sinko, Expert Opin. Deliv., 3, 419-435, 2006. 170. W. M. Padridge, Mol. Interv., 3, 90-105, 2003. 25