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010   $a981-16-0025-2
024 7 $a10.1007/978-981-16-0025-8
035   $a(CKB)4100000011781607
035   $a(MiAaPQ)EBC6509250
035   $a(Au-PeEL)EBL6509250
035   $a(OCoLC)1241447190
035   $a(PPN)253856701
035   $a(DE-He213)978-981-16-0025-8
035   $a(EXLCZ)994100000011781607
100   $a20210226d2021      u|         0
101 0 $aeng
135   $aurcnu||||||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aSoftware Reliability Growth Models /$fby David D. Hanagal, Nileema N. Bhalerao
205   $a1st ed. 2021.
210  1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2021.
215   $a1 online resource (115 pages) $cillustrations
225 1 $aInfosys Science Foundation Series in Mathematical Sciences,$x2364-4044
300   $aIncludes index.
311 08$a981-16-0024-4 
327   $a1. Introduction to Software Reliability Models -- 2. Literature Survey in Software Reliability Growth Models -- 3. NHPP Software Reliability Growth Models -- 4. Inverse Weibull Software Reliability Growth Model -- 5. Generalized Inverse Weibull Software Reliability Growth Model -- 6. Extended Inverse Weibull Software Reliability Growth Model -- 7. Generalized Extended Inverse Weibull Software Reliability Growth Model -- 8. Delayed S-Shaped SRGM with Time Dependent Fault Content Rate Function -- 9. Scope for Future Extension to SRGM.
330   $aThis book presents the basic concepts of software reliability growth models (SRGMs), ranging from fundamental to advanced level. It discusses SRGM based on the non-homogeneous Poisson process (NHPP), which has been a quite successful tool in practical software reliability engineering. These models consider the debugging process as a counting process characterized by its mean value function. Model parameters have been estimated by using either the maximum likelihood method or regression. NHPP SRGMs based on inverse Weibull, generalized inverse Weibull, extended inverse Weibull, generalized extended inverse Weibull, and delayed S-shaped have been focused upon. Review of literature on SRGM has been included from the scratch to recent developments, applicable in artificial neural networks, machine learning, artificial intelligence, data-driven approaches, fault-detection, fault-correction processes, and also in random environmental conditions. This book is designed for practitioners and researchers at all levels of competency, and also targets groups who need information on software reliability engineering.
410  0$aInfosys Science Foundation Series in Mathematical Sciences,$x2364-4044
606   $aStatistics
606   $aStatistics
615  0$aStatistics.
615 14$aStatistics.
676   $a605
700   $aHanagal$b David D.$0782092
702   $aBhalerao$b Nileema N.
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910484286203321
996   $aSoftware reliability growth models$91906797
997   $aUNINA