{"product_id":"software-reliability-growth-models-9789811600272","title":"Software Reliability Growth Models","description":"\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cblockquote\u003eThis book provides an introduction to software reliability growth models (SRGMs), focusing on non-homogeneous Poisson process (NHPP) based models. It covers various SRGMs, estimation methods, and applications in software reliability engineering, including artificial neural networks, machine learning, and data-driven approaches. It is designed for practitioners and researchers in the field. \u003c\/blockquote\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eFormat\u003c\/strong\u003e: Paperback \/ softback\u003cbr\u003e\u003cstrong\u003eLength\u003c\/strong\u003e: 104 pages\u003cbr\u003e\u003cstrong\u003ePublication date\u003c\/strong\u003e: 27 February 2022\u003cbr\u003e\u003cstrong\u003ePublisher\u003c\/strong\u003e: Springer Verlag, Singapore\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cbr\u003eThis comprehensive book delves into the realm of software reliability growth models (SRGMs), encompassing a range of concepts from fundamental to advanced levels. It focuses on SRGMs based on the non-homogeneous Poisson process (NHPP), which has proven to be an invaluable tool in practical software reliability engineering. These models recognize the debugging process as a counting process characterized by its mean value function. The estimation of model parameters is achieved through either the maximum likelihood method or regression, providing valuable insights into software reliability.\u003cbr\u003e\u003cbr\u003eNHPP SRGMs have been extensively studied, with particular attention given to inverse Weibull, generalized inverse Weibull, extended inverse Weibull, generalized extended inverse Weibull, and delayed S-shaped models. A comprehensive review of literature on SRGMs, spanning from its early beginnings to recent developments, is included. This review encompasses a wide range of applications, including artificial neural networks, machine learning, artificial intelligence, data-driven approaches, fault detection, fault correction processes, and even random environmental conditions.\u003cbr\u003e\u003cbr\u003eDesigned with practitioners and researchers in mind, this book caters to individuals at all levels of competency. It serves as a valuable resource for those seeking information on software reliability engineering, making it an essential tool for professionals and students alike.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWeight\u003c\/strong\u003e: 209g\u003cbr\u003e\u003cstrong\u003eDimension\u003c\/strong\u003e: 235 x 155 (mm)\u003cbr\u003e\u003cstrong\u003eISBN-13\u003c\/strong\u003e: 9789811600272\u003cbr\u003e \u003cstrong\u003eEdition number\u003c\/strong\u003e: 1st ed. 2021\u003c\/p\u003e","brand":"David D. Hanagal,Nileema N. Bhalerao","offers":[{"title":"Paperback \/ softback","offer_id":44103291175162,"sku":"9789811600272","price":74.96,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0522\/4297\/2845\/products\/noImage_1_2450c995-9e5b-4368-9f28-ded1b6a5842f.jpg?v=1667986870","url":"https:\/\/shulphink.com\/products\/software-reliability-growth-models-9789811600272","provider":"Shulph Ink","version":"1.0","type":"link"}