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Software reliability growth models

Hanagal, David D.

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Software reliability growth models

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Software reliability growth models/ by David D. Hanagal, Nileema N. Bhalerao.
作者:	Hanagal, David D.
其他作者:	Bhalerao, Nileema N.
出版者:	Singapore :Springer Singapore : : 2021.,
面頁冊數:	xxi, 104 p. :ill., digital ;24 cm.
內容註:	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.
Contained By:	Springer Nature eBook
標題:	Computer software - Reliability -
電子資源:	https://doi.org/10.1007/978-981-16-0025-8
ISBN:	9789811600258

Software reliability growth models
Hanagal, David D.

Software reliability growth models[electronic resource] /by David D. Hanagal, Nileema N. Bhalerao. - Singapore :Springer Singapore :2021. - xxi, 104 p. :ill., digital ;24 cm. - Infosys Science Foundation series in mathematical sciences. - Infosys Science Foundation series in mathematical sciences..

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.

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.

ISBN: 9789811600258

Standard No.: 10.1007/978-981-16-0025-8doiSubjects--Topical Terms:

3491628
Computer software
--Reliability

LC Class. No.: QA76.76.R44

Dewey Class. No.: 005

Software reliability growth models
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245 1 0 $a Software reliability growth models $h [electronic resource] / $c by David D. Hanagal, Nileema N. Bhalerao.
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505 0 $a 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.
520 $a 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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