Artificial neural network 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...
| Otros Autores: | , |
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| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Hoboken, New Jersey ; Beverly, Massachusetts :
John Wiley & Sons
2017.
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| Edición: | First edition |
| Colección: | Performability engineering series.
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| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009631485806719 |
| Sumario: | 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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| Descripción Física: | 1 online resource (220 pages) : illustrations, figures, tables |
| Formato: | Access using campus network via VPN at home (THEi Users Only). |
| Bibliografía: | Includes bibliographical references and index. |
| ISBN: | 9781119223962 9781119223924 9781119223931 |
