A Bayesian hidden Markov model for software failures

The paper presents the Bayesian analysis of a new model in software reliability, aimed to detect deterioration due to potential introduction of new bugs into the software during the development phase. Since the introduction of bugs is an unobservable process, latent variables are introduced to incorporate this characteristic into the proposed hidden Markov model. The state of the latent process reflects the effectiveness of the interventions, that is, the design changes to the software after each observed failure time. The Bayesian estimation (via MCMC) of the state of the latent process is a straightforward matter, if the dimension of the state space is known. Otherwise, we consider dimension selection as a Bayesian model selection problem and we also examine the relationship between possible overfitting and the choice of hyperparameters.