02894nam 2200565 450 991082817000332120231026213040.01-119-22396-21-119-22392-X1-119-22393-8(CKB)4330000000009391(Au-PeEL)EBL5056371(CaPaEBR)ebr11449804(CaONFJC)MIL1039887(OCoLC)998753739(CaSebORM)9781119223542(MiAaPQ)EBC5056371(EXLCZ)99433000000000939120171017h20172017 uy 0engurcnu||||||||rdacontentrdamediardacarrierArtificial neural network for software reliability prediction /by Manjubala Bisi and Neeraj Kumar GoyalFirst editionHoboken, New Jersey ;Beverly, Massachusetts :John Wiley & Sons :Scrivener Publishing,2017.©20171 online resource (220 pages) illustrations, figures, tablesPerformability engineering series.THEi Wiley ebooks.1-119-22354-7 Includes bibliographical references and index.Introduction -- Software reliability modelling -- Prediction of cumulative number of software failures -- Prediction of time between successive software failures -- Identification of software fault-prone modules -- Prediction of software development efforts -- Recent trends in software reliability.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.Performability engineering series.Neural networks (Computer science)Computer softwareReliabilityNeural networks (Computer science)Computer softwareReliability.006.32Bisi Manjubala1667683Goyal Neeraj KumarMiAaPQMiAaPQMiAaPQBOOK9910828170003321Artificial neural network for software reliability prediction4027691UNINA