This book focuses on a specialized branch of the vast domain of software engineering: component-based software engineering (CBSE). Component-Based Software Engineering: Methods and Metrics 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: (1) reusability issues, (2) interaction and integration issues, and (3) testing and reliability issues. This book covers the state-of-the-art literature survey of at least 20 years in the domain of reusability, interaction and integration complexities, and 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.
Features:
- 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 to this book to understand the concepts from scratch. These measures and metrics can be used to estimate the software before the actual coding commences.
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.
