This book addresses software faults-a critical issue that not only reduces the quality of software, but also increases their development costs. Various models for predicting the fault-proneness of software systems have been proposed; however, most of them provide inadequate information, limiting their effectiveness. This book focuses on the prediction of number of faults in software modules, and provides readers with essential insights into the generalized architecture, different techniques, and state-of-the art literature. In addition, it covers various software fault datasets and issues that crop up when predicting number of faults. A must-read for readers seeking a "one-stop" source of information on software fault prediction and recent research trends, the book will especially benefit those interested in pursuing research in this area. At the same time, it will provide experienced researchers with a valuable summary of the latest developments.

Autorentext
Dr. Santosh Singh Rathore is currently working as an Assistant Professor at the Department of Computer Science and Engineering, National Institute of Technology (NIT) Jalandhar, India. He received his Ph.D. degree from the Indian Institute of Technology Roorkee (IIT) and his master's degree (M.Tech.) from the Indian Institute of Information Technology Design and Manufacturing (IIITDM) in 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 in various peer-reviewed journals and international conference proceedings.

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 is currently engaged in various national and international research/consultancy projects and has many accolades to his credit, e.g. a Young Faculty Research Fellowship from the MeitY (Govt. of India), NSF/TCPP early adopter award2014, 2015, ITS Travel Award 2011 and 2013, etc. 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.

Klappentext
This book addresses software faultsa critical issue that not only reduces the quality of software, but also increases their development costs. Various models for predicting the fault-proneness of software systems have been proposed; however, most of them provide inadequate information, limiting their effectiveness. This book focuses on the prediction of number of faults in software modules, and provides readers with essential insights into the generalized architecture, different techniques, and state-of-the art literature. In addition, it covers various software fault datasets and issues that crop up when predicting number of faults.
A must-read for readers seeking a one-stop source of information on software fault prediction and recent research trends, the book will especially benefit those interested in pursuing research in this area. At the same time, it will provide experienced researchers with a valuable summary of the latest developments.

Inhalt

1. Introduction1.1 Software Fault Prediction1.2 Software Fault Terminologies1.3 Advantages of Software Fault Prediction1.4 Contributions1.5 Summary1.6 References2. Prediction of the Number of Faults2.1 Introduction2.2 Architecture of Fault Prediction Process2.3 Components of Fault Prediction model2.4 Summary2.5 References3. Software Fault Datasets and Their Issues3.1 Introduction3.2 Repositories for Software Fault Datasets3.3 Fault Dataset Issues3.4 Summary3.5 References4. Evaluation of techniques for the prediction of number of faults4.1 Introduction4.2 Technique Descriptions4.3 Performance Measures4.4 Results and Analysis4.5 Summary4.6 References5. Ensemble Method for the prediction of Number of Faults5.1 Introduction5.2 Ensemble method5.3 Performance Measures5.4 Experimental Analysis5.5 Discussion5.6 Summary5.7 References6. Conclusions