Software reliability: measurement, prediction, application
Software reliability: measurement, prediction, application
Software testing techniques (2nd ed.)
Software testing techniques (2nd ed.)
Projecting Software Defects from Analyzing Ada Designs
IEEE Transactions on Software Engineering - Special issue on software measurement principles, techniques, and environments
Measuring the software process: a practical guide to functional measurements
Measuring the software process: a practical guide to functional measurements
Handbook of software reliability engineering
Handbook of software reliability engineering
Applied software measurement (2nd ed.): assuring productivity and quality
Applied software measurement (2nd ed.): assuring productivity and quality
Software Engineering Economics
Software Engineering Economics
Software system defect content prediction from development process and product characteristics
Software system defect content prediction from development process and product characteristics
Software Reliability Status and Perspectives
IEEE Transactions on Software Engineering
Software Reliability Models: Assumptions, Limitations, and Applicability
IEEE Transactions on Software Engineering
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Most existing software reliability models estimate the failure intensity function during dynamic testing. While these models are useful engineering tools, they can not be applied to earlier life-cycle phases where pay-off is maximum in terms of avoiding later failures. Few models have been developed to capture phenomena occurring early in the life cycle and their impact on reliability and much research effort is still needed in this area. Our previous research efforts have led to the development of a stochastic model which relates human errors committed during software development and debugging activities to the software failure intensity function. The software development schedule and other influencing factors (e.g., experience, schedule pressure, etc) were used to predict human error rates. The model is based on several assumptions. In particular, it is assumed that repair is instantaneous. The present paper attempts to remove this assumption and assesses the impact of repair times on previous findings.