Computer software reliability has never been so important. Computers are used in areas as diverse as air traffic control, nuclear reactors, real-time military, industrial process control, security system control, biometric scan-systems, automotive, mechanical and safety control, and hospital patient monitoring systems. Many of these applications require critical functionality as software applications increase in size and complexity. This book is an introduction to software reliability engineering and a survey of the state-of-the-art techniques, methodologies and tools used to assess the reliability of software and combined software-hardware systems. Current research results are reported and future directions are signposted. This text will interest: graduate students as a course textbook introducing reliability engineering software; reliability engineers as a broad, up-to-date survey of the field; and researchers and lecturers in universities and research institutions as a one-volumereference.

Giving an accessible introduction to software and systems reliability engineering whilst being up to date with current developments in this field Includes supplementary material: sn.pub/extras

Autorentext

Dr Hoang Pham is a professor in the Department of Industrial and Systems Engineering at Rutgers, The State University of New Jersey. Before joining Rutgers, he was a senior engineering specialist at the Boeing Company, Seattle, and the Idaho National Engineering Laboratory, Idaho Falls. His research interests include software reliability, system reliability modeling, maintenance, fault-tolerant computing, and biological systemability-risk assessment.

Klappentext

Inhalt
System Reliability Concepts.- Theory of Estimation.- Software Development Lifecycle and Data Analysis.- Software Reliability Modeling.- Imperfect-debugging Models.- Testing Coverage and Removal Models.- Software Reliability Models with Environmental Factors.- Calibrating Software Reliability Models.- Optimal Release Policies.- Complex Fault-tolerant System Reliability Modeling.