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Fault Prediction Modeling for the Prediction of Number of Software Faults

Benefits

  • Illustrates the process of number of fault prediction

  • Features special chapters on number of fault prediction and ensemble methods

  • Broadens readers’ understanding with an empirical study on learning models

Book
  • 1.5k Downloads

Part of the SpringerBriefs in Computer Science book series (BRIEFSCOMPUTER)

Table of contents

  1. Front Matter
    Pages i-xiii
  2. Santosh Singh Rathore, Sandeep Kumar
    Pages 1-9
  3. Santosh Singh Rathore, Sandeep Kumar
    Pages 11-29
  4. Santosh Singh Rathore, Sandeep Kumar
    Pages 31-45
  5. Santosh Singh Rathore, Sandeep Kumar
    Pages 47-58
  6. Santosh Singh Rathore, Sandeep Kumar
    Pages 59-69
  7. Santosh Singh Rathore, Sandeep Kumar
    Pages 71-73
  8. Back Matter
    Pages 75-78

About this book

Introduction

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.
 



Keywords

Number of Fault Prediction Ensemble Methods Software Engineering Software Fault Prediction Quality Assurance Testing Soft Computing and Machine Learning Learning Models

Authors and affiliations

  1. 1.Department of Computer Science and EngineeringABV-Indian Institute of Information Technology and Management GwaliorGwaliorIndia
  2. 2.Department of Computer Science and EngineeringIndian Institute of Technology RoorkeeRoorkeeIndia

About the authors

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 award—2014, 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.

Bibliographic information

  • Book Title Fault Prediction Modeling for the Prediction of Number of Software Faults
  • Authors Santosh Singh Rathore
    Sandeep Kumar
  • Series Title SpringerBriefs in Computer Science
  • Series Abbreviated Title SpringerBriefs Computer Sci.
  • DOI https://doi.org/10.1007/978-981-13-7131-8
  • Copyright Information The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2019
  • Publisher Name Springer, Singapore
  • eBook Packages Computer Science Computer Science (R0)
  • Softcover ISBN 978-981-13-7130-1
  • eBook ISBN 978-981-13-7131-8
  • Series ISSN 2191-5768
  • Series E-ISSN 2191-5776
  • Edition Number 1
  • Number of Pages XIII, 78
  • Number of Illustrations 7 b/w illustrations, 1 illustrations in colour
  • Topics Software Engineering
    The Computer Industry
  • Buy this book on publisher's site
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