A Markov modulated Poisson model for software reliability

In this paper we consider a latent Markov process governing the intensity rate of software failures. The latent process represents the behavior of the debugging operations over time and enables us to deal with the imperfect debugging scenario. We develop the Bayesian inference for the model and also introduce a method to infer the unknown dimension of the Markov process. We illustrate the implementation of our model and the Bayesian approach by using actual software failure data.