This 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.

Discusses the basic concepts of software reliability growth models Explains different non-homogeneous Poisson process of software reliability models Presents applications in artificial neural networks, machine learning, and artificial intelligence Appeals to practitioners, researchers, and students who need information on software reliability engineering

Autorentext

David D. Hanagal is an Honorary Professor at the Symbiosis Statistical Institute, Symbiosis International University, Pune, India. He was previously a professor at the Department of Statistics, Savitribai Phule Pune University, India. An elected fellow of the Royal Statistical Society, UK, he is an editor and on the editorial board of several respected international journals. He has authored two books and published over 125 research publications in leading journals. With 30 years of research experience, he is an expert on writing programs using SAS, R, MATLAB, MINITAB, SPSS, and SPLUS. He also has worked as a visiting professor at several universities in the USA, Germany, and Mexico, and delivered a number of talks at conferences around the globe. His research interests include statistical inference, selection problems, reliability, survival analysis, frailty models, Bayesian inference, stress-strength models, Monte Carlo methods, MCMC algorithms, bootstrapping, censoring schemes, distribution theory, multivariate models, characterizations, repair and replacement models, software reliability, quality loss index, and nonparametric inference.

Inhalt

1. 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.