Component Based Software Engineering : Methods and Metrics book cover
1st Edition

Component Based Software Engineering
Methods and Metrics

  • Available for pre-order. Item will ship after November 19, 2020
ISBN 9780367354886
November 19, 2020 Forthcoming by Chapman and Hall/CRC
224 Pages - 90 B/W Illustrations

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Book Description

Component Based Software Engineering: Methods and Metrics focuses on a specialized branch of the vast domain of software engineering, that is, Component-based software engineering (CBSE). This book enhances the basic understanding of components by defining categories, characteristics, repository, interaction, complexity and composition. It divides the research domain of CBSE into three major sub-domains: Reusability issues, Interaction and integration issues and Testing and reliability issues. This book covers the state-of-art literature survey of atleast 20 years in the domain of reusability, interaction and integration complexities, testing and reliability issues of component-based software engineering.

The aim of this book is not only to review and analyze the previous works conducted by eminent researchers, academicians and organizations in the context of CBSE, but also suggests innovative, efficient and better solutions. A rigorous and critical survey of traditional and advanced paradigms of software engineering is provided in the book.


    • In-interactions and Out-Interactions both are covered to assess the complexity.
    • In the context of CBSE both white-box and black-box testing methods and their metrics are described.
    • This work covers reliability estimation using reusability which is an innovative method.
    • Case studies and real life software examples are used to explore the problems and their solutions.

Students, research scholars, software developers and software designers or individuals interested in software engineering, especially in component-based software engineering can refer this book to understand the concepts from the scratch. These measures and metrics can be used to estimate the software before the actual coding commences.

Table of Contents

Introduction To Software Engineering And Component Based Software Engineering. Introduction. Software Engineering.Paradigms of Software Engineering Component. Traditional Software Engineering Paradigms. Advanced Software Engineering Paradigms. Component Based Software Engineering (CBSE). Summary. References. Components Based Development Models. Introduction. Y Life Cycle Model. BRIDGE Model. X Model. Umbrella model. Knot Model. Elite Model. Summary. References. Major Issues in Component Based Software Engineering. Introduction. Reuse and Reusability Issues. Interaction and Integration Issues. Complexity Issues. Design and Coding Issues. Testing Issues. Reliability Issues. Quality Issues. Summary. References. Reusability-Metric Based Selection and Verification of Components. Introduction. Reuse and Reusability. Reusable Artefacts and Work Products. Reusability Metrics. Components Selection and Verification Using Reusability-Metric. Summary. References. Interaction and Integration Complexity Metrics for Component-Based Software. Introduction. In-Out-Interaction Complexity. Cyclomatic Complexity for Component-Based Software. Summary. References. Component Based Software Testing Techniques and Methods for Test Case Generation. Introduction. Testing Techniques. Testing Levels for Component Based Software. Test Case Generation Technique for Black-Box Components. Test Case Generation Technique for White-Box Components. Summary. References. Estimating Execution Time and Reliability of Component Based Software. Introduction. Interaction-Metric. Case Study and Result Discussion. Reliability Estimation. Reliability Estimation Method Using Reusability-Metrics. Summary. References.

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Umesh Kumar Tiwari is working as an Associate Professor in Department of Computer Science and Engineering in Graphic Era (Deemed to be University), Dehradun. He had received Ph.D from Graphic Era (Deemed to be University) in 2016 and Master of Computer Applications in 2005. He has more than 13 years of research and teaching experience in AICTE/UGC approved universities and colleges at Undergraduate and Postgraduate levels. He is author of 2 books including "Principles of Programming Languages" and "Paradigms of Programming Languages". He is supervising Ph.D. and M. Tech students in their Thesis. He has published more than 25 research papers in National and International Journals/conferences in the field of Software Engineering, Wireless Networks, WSN, and IoT. His research interest includes Component Based Software Engineering, Wireless Networks, WSN, IoT, and Agile Methodology.

Santosh Kumar received Ph.D. from IIT Roorkee (India) in 2012, M. Tech. (CSE) from Aligarh Muslim University, Aligarh (India) in 2007 and B.E. (IT) from C.C.S. University, Meerut (India) in 2003. He has more than 13 years of experience in teaching/research of UG (B. Tech.) and PG (M. Tech) level courses as a Lecturer/Assistant Professor/Associate Professor in various academic /research organizations. He has supervised 01 Ph.D. Thesis, 22 M. Tech Thesis, 18 B. Tech projects and presently mentoring 06 Ph.D. students (singly and jointly) and 08 B. Tech. students. He has also completed a consultancy project titled "MANET Architecture Design for Tactical Radios" of DRDO, Dehradun in between 2009-2011. He is an active reviewer board member in various National/International Journals (i.e. IEEE Transection on Computational Social Systems, IEEE Access, ACM Transection on Cyber-physical system, Soft Computing-Springer etc.) and Conferences. He has memberships of ACM (Senior Member), IEEE, IAENG, ACEEE, ISOC (USA) and contributed more than 50 research papers in National and International Journals/conferences in the field of Software Engineering, Wireless Networks, WSN, IoT and Machine Learning. Currently he is holding position of Associate Professor in the Graphic Era Deemed to be University, Dehradun (India). His research interest includes Software Engineering, Wireless Networks, WSN, IoT and Machine Learning.