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Artificial Neural Network Applications for Software Reliability Prediction.
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, faul...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Wiley,
2017.
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| Colección: | Performability Engineering Ser.
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| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 100 | 1 | |a Bisi, Manjubala. | |
| 245 | 1 | 0 | |a Artificial Neural Network Applications for Software Reliability Prediction. |
| 260 | |b Wiley, |c 2017. | ||
| 300 | |a 1 online resource (312 pages) | ||
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| 490 | 1 | |a Performability Engineering Ser. | |
| 588 | 0 | |a Print version record. | |
| 505 | 0 | |a Title page -- Copyright page -- Dedication -- Preface -- Acknowledgement -- Abbreviations -- Chapter 1: Introduction -- 1.1 Overview of Software Reliability Prediction and Its Limitation -- 1.2 Overview of the Book -- 1.3 Organization of the Book -- Chapter 2: Software Reliability Modelling -- 2.1 Introduction -- 2.2 Software Reliability Models -- 2.3 Techniques used for Software Reliability Modelling -- 2.4 Importance of Artificial Neural Network in Software Reliability Modelling -- 2.5 Observations -- 2.6 Objectives of the Book | |
| 505 | 8 | |a Chapter 3: Prediction of Cumulative Number of Software Failures3.1 Introduction -- 3.2 ANN Model -- 3.3 Experiments -- 3.4 ANN-PSO Model -- 3.5 Experimental Results -- 3.6 Performance Comparison -- Chapter 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 -- Chapter 5: Identification of Software Fault-Prone Modules -- 5.1 Research Background -- 5.2 ANN Model -- 5.3 ANN-PSO Model -- 5.4 Discussion of Results | |
| 505 | 8 | |a Chapter 6: Prediction of Software Development Efforts6.1 Need for Development Efforts Prediction -- 6.2 Efforts Multipliers Affecting Development Efforts -- 6.3 Artificial Neural Network Application for Development Efforts Prediction -- 6.4 Performance Analysis on Data Sets -- Chapter 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 | |
| 520 | |a 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. | ||
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