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Artificial Neural Network Applications for Software Reliability Prediction (Performability Engineering Series) Hardback - 2017
by Bisi, Manjubala
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- Hardback
- first
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Details
- Title Artificial Neural Network Applications for Software Reliability Prediction (Performability Engineering Series)
- Author Bisi, Manjubala
- Binding Hardback
- Edition 1
- Condition New
- Pages 313
- Volumes 1
- Language ENG
- Publisher Wiley-Interscience
- Publication date 2017-09-12
- Features Bibliography, Index
- Bookseller's Inventory # DADAX1119223547
- ISBN 9781119223542 / 1119223547
- Weight 1.3 lbs (0.59 kg)
- Dimensions 9 x 6 x 0.75 in (22.86 x 15.24 x 1.91 cm)
- Size 6.00x0.75x9.00
- Category Computers - General Information
- Library of Congress subjects Neural networks (Computer science), Computer software - Reliability
- Library of Congress Catalogue Number 2017030704
- Dewey Decimal Code 006.32
- Quantity available 1
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From the publisher
From the rear cover
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.
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.
Audience
The book will be invaluable to software researchers and practitioners working in reliability prediction.
