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Artificial Neural Network Applications for Software Reliability Prediction

By Manjubala Bisi and Neeraj Kumar Goyal
Series: Performability Engineering Series
Copyright: 2017   |   Status: Published
ISBN: 9781119223542  |  Hardcover  |  
310 pages | 62 illustrations
Price: $195 USD
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One Line Description
This book provides a starting point for software professionals to apply artificial neural networks for software reliability prediction without having analyst capability and expertise in various ANN architectures and their optimization.

Audience
Researchers working in software reliability area and software practitioners interested in reliability prediction are the primary audience for this book. Institutions / Organizations, where software reliability is considered as a prescribed course work for the postgraduate students will also benefitted from work presented in this book.

Description
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 are presented. 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.

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Author / Editor Details
Manjubala Bisi is currently an Assistant Professor in the Computer Science and Engineering Department, Kakatiya Institute of Technology and Science, Warangal, Telengana, India. She received her PhD from the Indian Institute of Technology Kharagpur in Reliability Engineering in 2015. Her research interests include software reliability modelling, artificial neural networks and soft computing techniques.

Neeraj Kumar Goyal is currently an Associate Professor in Subir Chowdhury School of Quality and Reliability, Indian Institute of Technology Kharagpur, India. He received his PhD from IIT Kharagpur in Reliability Engineering in 2006. His major areas of research are network /system reliability and software reliability. He has completed various research and consultancy projects for various organizations, e.g. DRDO, NPCIL, Vodafone, ECIL etc. He has contributed research papers to refereed international journals and conference proceedings.

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Table of Contents
Preface
Acknowledgement
Abbreviations i
1 Introduction
1.1 Overview of Software Reliability Prediction and Its Limitation
1.2 Overview of the Book
1.2.1 Predicting Cumulative Number of Software
Failures in a Given Time
1.2.2 Predicting Time Between Successive Software Failures
1.2.3 Predicting Software Fault-Prone Modules
1.2.4 Predicting Software Development Efforts
1.3 Organization of the Book
2 Software Reliability Modelling
2.1 Introduction
2.2 Software Reliability Models
2.2.1 Classification of Existing Models
2.2.2 Software Reliability Growth Models
2.2.3 Early Software Reliability Prediction Models
2.2.4 Architecture based Software Reliability Prediction Models
2.2.5 Bayesian Models
2.3 Techniques used for Software Reliability Modelling
2.3.1 Statistical Modelling Techniques
2.3.2 Regression Analysis
2.3.3 Fuzzy Logic
2.3.3.1 Fuzzy Logic Model for Early Fault Prediction
2.3.3.2 Prediction and Ranking of Fault-prone Software Modules using Fuzzy Logic
2.3.4 Support Vector Machine
2.3.4.1 SVM for Cumulative Number of Failures Prediction
2.3.5 Genetic Programming
2.3.6 Particle Swarm Optimization
2.3.7 Time Series Approach
2.3.8 Naive Bayes
2.3.9 Artificial Neural Network
2.4 Importance of Artificial Neural Network in Software Reliability Modelling
2.4.1 Cumulative Number of Software Failures Prediction
2.4.2 Time Between Successive Software Failures Prediction
2.4.3 Software Fault-Prone Module Prediction
2.4.4 Software Development Efforts Prediction
2.5 Observations
2.6 Objectives of the Book
3 Prediction of Cumulative Number of Software Failures
3.1 Introduction
3.2 ANN Model
3.2.1 Artificial Neural Network Model with Exponential Encoding
3.2.2 Artificial Neural Network Model with Logarithmic Encoding
3.2.3 System Architecture
3.2.4 Performance Measures
3.3 Experiments
3.3.1 Effect of Different Encoding Parameter
3.3.2 Effect of Different Encoding Function
3.3.3 Effect of Number of Hidden Neurons
3.4 ANN-PSO Model
3.4.1 ANN Architecture
3.4.2 Weight and Bias Estimation Through PSO
3.5 Experimental Results
3.6 Performance Comparison
4 Prediction of Time Between Successive Software Failures
4.1 Time Series Approach in ANN
4.2 ANN Model
4.3 ANN- PSO Model
4.4 Results and Discussion
4.4.1 Results of ANN Model
4.4.2 Results of ANN-PSO Model
4.4.3 Comparison
5 Identification of Software Fault-Prone Modules
5.1 Research Background
5.1.1 Software Quality Metrics Affecting Fault-Proneness
5.1.2 Dimension Reduction Techniques
5.2 ANN Model
5.2.1 SA-ANN Approach
5.2.1.1 Logarithmic Scaling Function
5.2.1.2 Sensitivity Analysis on Trained ANN
5.2.2 PCA-ANN Approach
5.3 ANN-PSO Model
5.4 Discussion of Results
5.4.1 Results of ANN Model
5.4.1.1 SA-ANN Approach Results
5.4.1.2 PCA-ANN Approach Results
5.4.1.3 Comparison Results of ANN Model
5.4.2 Results of ANN-PSO Model
5.4.2.1 Reduced Data Set
5.4.2.2 Comparison Results of ANN-PSO Model
6 Prediction of Software Development Efforts
6.1 Need for Development Efforts Prediction
6.2 Efforts Multipliers Affecting Development Efforts
6.3 Artificial Neural Network Application for
Development Efforts Prediction
6.3.1 Additional Input Scaling Layer ANN Architecture
6.3.2 ANN-PSO Model
6.3.3 ANN-PSO-PCA Model
6.3.4 ANN-PSO-PCA-GA Model
6.3.4.1 Chromosome Design and Fitness Function
6.3.4.2 System Architecture of ANN-PSOPCA-GA Model
6.4 Performance Analysis on Data Sets
6.4.1 COCOMO Data Set
6.4.2 NASA Data Set
6.4.3 Desharnais Data Set
6.4.4 Albrecht Data Set
7 Recent Trends in Software Reliability
References
APPENDIX Failure Count Data Set
APPENDIX Time Between Failure Data Set
APPENDIX CM1 Data Set
APPENDIX COCOMO 63 Data Set
Index

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