Wide coverage of important topics

Coverage of binary class as well as number of fault prediction

Special chapter on number of fault prediction

Many figures and tables for better illustration Empirical study of learning models

Includes coverage of binary class as well as number of fault prediction Features a wealth of figures and tables to better illustrate the content Presents an empirical study on learning models

Autorentext

Dr Sandeep Kumar is currently working as an Assistant Professor at the Department of Computer Science and Engineering, Indian Institute of Technology (IIT) Roorkee, India. His areas of interest include Semantic Web, Web Services, and Software Engineering. He has supervised many Ph.D. students and filed two patents for his work done along with students. He is currently handling multiple national and international research/consultancy projects and has many accolades to his creditYoung Faculty Research Fellowship of MeitY (Govt. of India), NSF/TCPP early adopter award-2014, 2015, ITS Travel Award 2011 and 2013 and others. He is a member of the ACM and senior member of the IEEE. His name has also been listed in major directories such as Marquis Who's Who, IBC and others.

Dr Santosh Singh Rathore is currently working as an Assistant Professor in the Department of Computer Science and Engineering, National Institute of Technology (NIT) Jalandhar,India. He received his PhD degree from the Indian Institute of Technology Roorkee (IITR) and his master's degree (M.Tech.) from the Indian Institute of Information Technology Design and Manufacturing (IIITDM) Jabalpur, India. His research interests include Software Fault Prediction, Software Quality Assurance, Empirical Software Engineering, Object-Oriented Software Development and Object-Oriented Metrics. He has published research papers in various peer-reviewed journals and international conference proceedings.

Zusammenfassung

Wide coverage of important topics

Coverage of binary class as well as number of fault prediction

Special chapter on number of fault prediction

Many figures and tables for better illustration Empirical study of learning models

Inhalt
Chapter 1 . Introduction.- Chapter 2 . Software Fault Prediction Process.- Chapter 3 . Types of Software Fault Prediction.- Chapter 4 . Software Fault Dataset.- Chapter 5. Evaluation of Techniques for Binary Class Prediction.- Chapter 6. Number of Fault Prediction.- Chapter 7. Conclusions.