Artificial Neural Netork Applications for Softare Reliability Prediction [Hardcover]

Artificial neural network (ANN) has proven to be a universal approximator for any non-linear continuous function with arbitrary accuracy. This book presents how to apply ANN to measure various software reliability indicators: number of failures in a given time, time between successive failures, fault-prone modules and development efforts. The application of machine learning algorithm i.e. artificial neural networks application in software reliability prediction during testing phase as well as early phases of software development process is presented as well. Applications of artificial neural network for the above purposes are discussed with experimental results in this book so that practitioners can easily use ANN models for predicting software reliability indicators.

Preface xi

Acknowledgement xv

Abbreviations xvii

1 Introduction 1

1.1 Overview of Software Reliability Prediction and Its Limitation 6

1.2 Overview of the Book 8

1.2.1 Predicting Cumulative Number of Software Failures in a Given Time 9

1.2.2 Predicting Time Between Successive Software Failures 11

1.2.3 Predicting Software Fault-Prone Modules 13

1.2.4 Predicting Software Development Efforts 15

1.3 Organization of the Book 17

2 Software Reliability Modelling 19

2.1 Introduction 19

2.2 Software Reliability Models 20

2.2.1 Classification of Existing Models 21

2.2.2 Software Reliability Growth Models 25

2.2.3 Early Software Reliability Prediction Models 27

2.2.4 Architecture based Software Reliability Prediction Models 29

2.2.5 Bayesian Models 31

2.3 Techniques used for Software Reliability Modelling 31