A Bayes nonparametric framework for software-reliability analysis

This paper presents a Bayes nonparametric approach for tracking and predicting software reliability. We use the common assumptions on the software operational environment to get a stochastic model where the successive times between software failures are exponentially distributed; their failure rates have Markov priors. Under these general assumptions we give Bayes estimates of the parameters that assess and predict the software reliability. We give algorithms (based on Monte-Carlo methods) to compute these Bayes estimates. Our approach allows the reliability analyst to construct a personal software reliability model simply by specifying the available prior knowledge; afterwards the results in this paper can be used to get Bayes estimates of the useful reliability parameters. Examples of possible prior physical knowledge concerning the software testing and correction environments are given. The maximum-entropy principle is used to translate this knowledge to prior distributions on the failure-rate process. Our approach is used to study some simulated and real failure data sets