Bayes inference for S-shaped software-reliability growth models

Abstract

Bayes inference for a nonhomogeneous Poisson process with an S-shaped mean value function is studied. In particular, the authors consider the model of Ohba et al. (1983), and its generalization to a class of gamma distribution growth curves. Two Gibbs sampling approaches are proposed to compute the Bayes estimates of the mean number of errors remaining and the current system reliability. One algorithm is a Metropolis within Gibbs algorithm, The other is a stochastic substitution algorithm with data augmentation. Model selection based on the posterior Bayes factor is studied. A numerical example with simulated data is given.