Bayesian Modeling and Estimations for Reliability Predictions of Software Debugging Processes Based on the Weibull distribution

Abstract

A Bayesian dynamic method for analysis of software debugging process data is handled. It is addressed to predict states of software reliability. In the Bayesian analysis, hierarchical prior models are structured, and empirical and expert knowledge priors are supposed. These priors play roles recognized as representations for complex situations. The empirical prior based on observed data is used for the representation of uncertainty corrections. The prior of success probability for the tests is assumed based on expert knowledge of engineers. The reliability is estimated based on the posterior mean of the failure states. The Bayesian inferences are derived based on the computational simulation methods, and the information criterion EIC is used to choose appropriate models.