P.K. Kapur

Software Reliability Assessment with OR Applications is a comprehensive guide to software reliability measurement, prediction, and control. It provides a thorough understanding of the field and gives solutions to the decision-making problems that concern software developers, engineers, practitioners, scientists, and researchers. Using operations research techniques, readers will learn how to solve problems under constraints such as cost, budget and schedules to achieve the highest possible quality level. Software Reliability Assessment with OR Applications is a comprehensive text on software engineering and applied statistics, state-of-the art software reliability modeling, techniques and methods for reliability assessment, and related optimization problems. It addresses various topics, including:unification methodologies in software reliability assessment; application of neural networks to software reliability assessment;software reliability growth modeling using stochastic differential equations;software release time and resource allocation problems; andoptimum component selection and reliability analysis for fault tolerant systems.Software Reliability Assessment with OR Applications is designed to cater to the needs of software engineering practitioners, developers, security or risk managers, and statisticians. It can also be used as a textbook for advanced undergraduate or postgraduate courses in software reliability, industrial engineering, and operations research and management.

Software Reliability Assessment with OR Applications is a comprehensive guide to software reliability measurement, prediction, and control. It provides a thorough understanding of the field and gives solutions to the decision-making problems that concern software developers, engineers, practitioners, scientists, and researchers. Using operations research techniques, readers will learn how to solve problems under constraints such as cost, budget and schedules to achieve the highest possible quality level. Software Reliability Assessment with OR Applications is a comprehensive text on software engineering and applied statistics, state-of-the art software reliability modeling, techniques and methods for reliability assessment, and related optimization problems. It addresses various topics, including:unification methodologies in software reliability assessment; application of neural networks to software reliability assessment;software reliability growth modeling using stochastic differential equations;software release time and resource allocation problems; andoptimum component selection and reliability analysis for fault tolerant systems.Software Reliability Assessment with OR Applications is designed to cater to the needs of software engineering practitioners, developers, security or risk managers, and statisticians. It can also be used as a textbook for advanced undergraduate or postgraduate courses in software reliability, industrial engineering, and operations research and management.

With better computing facilities now available, there is an ever-increasing need to ensure that elegant theoretical results on hardware reliability are computationally available. This book discusses those aspects which have relevance to computing systems and those where numerical computation was a problem. It is also well known that nearly 70% of the cost goes into software development and hence software reliability assumes special importance. The book not only gives an extensive review of the literature on software reliability but also provides direction in developing models which are flexible and can be used in a variety of testing environments. Besides, several alternative formulations of the release time problem are discussed along with variants such as allocation of testing effort resources to different modules of the software, or the testing effort control problem. Software reliability has now emerged as an independent discipline and requires a strong partnership between computer scientists, statisticians and operational researchers. This aspect is broadly highlighted in the book.