Software Reliability Growth Models (eBook, PDF) - Hanagal, David D.; Bhalerao, Nileema N.
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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…mehr

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Produktbeschreibung
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.

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Autorenporträt
David D. Hanagal is Honorary Professor at Symbiosis Statistical Institute, Symbiosis International University, Pune, India. He was previously Professor at the Department of Statistics, Savitribai Phule Pune University, India. He is an elected fellow of the Royal Statistical Society, UK. He has authored three books and three book chapters and published over 130 research publications in leading international refereed journals. He has guided 9 Ph.D. students in different areas of statistics, namely reliability, survival analysis, frailty models, repair and replacement models, software reliability, and quality loss index. He also has worked as Visiting Professor at several universities in the USA, Germany, and Mexico, and delivered more than 100 invited talks in many national and international platforms of repute worldwide.
He is Editor-in-Chief, Associate Editor, and an editorial board member of several reputed national and international journals. He is the chairperson, the subject expert, and an advisory committee member on several UGC committees. He is a National Assessment and Accreditation Council (NAAC) Assessor from UGC. He is the subject expert (statistics) in board of studies committee of several universities. He is a governing council member, an executive council member, and a life member of several statistical societies, organizations, and associations. 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. With more than 40 years of teaching experience and more than 35 years of research experience, he is an expert on writing programs using SAS, R, MATLAB, MINITAB, SPSS, and SPLUS statistical packages.
Nileema N. Bhalerao is Assistant Professor at Fergusson College, Pune, India. With 20 years of teaching experience and 5 years of research experience, she is an expert on writing programs using several statistical packages. Her research interest includes software reliability models.