Software Reliability Assessment with OR Applications [electronic resource] / by P.K. Kapur, Hoang Pham, A. Gupta, P.C. Jha.

1. Introduction -- 2. Software Reliability Growth Models -- 3. Imperfect Debugging / Testing Efficiency Software Reliability Growth Models -- 4. Testing-Coverage and Testing-Domain Models -- 5. Change Point Models -- 6 -- Unification of SRGM -- 7. Artificial Neural Networks Based SRGM -- 8. SRGM Using SDE -- 9. Discrete SRGM -- 10. Software Release Time Decision Problems Introduction -- 11. Allocation Problems at Unit Level Testing -- 12. Fault Tolerant Systems.

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; and optimum 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.