TEST STATISTICS FOR SYSTEM DESIGN FAILURE
John H R May, Silke Kuball, Gordon A. Hughes · International Journal of Reliability Quality and Safety Engineering · 1999
A structural model for the estimation of software failure rates is proposed which is based on a partition of the code into components. Systematic failure is assumed to be induced by the interaction of these components. A Bayesian inference scheme is used to perform failure rate estimation on the basis of N failure free test runs. The approach splits the problem of constructing a system prior up into the smaller, conceivably simpler problems of constructing subtask priors. Thereby a wider range of prior information is used in the process of assessing system safety. The work in this paper constitutes a first step towards a formal statistical understanding of the relationship between system complexity and system testing for reliability. Long-term implications are the achievement of more informative reliability estimates and guidelines on cost-effective testing.