Bayesian Inference for S-Shaped Software Reliability Growth Models

Reader Aids { Purpose: Widen state of the art Special math needed for explanation: Stochastic processes, Statistical inference Special math needed to use results: Same Results useful to: Software reliability theoreticians Summary & Conclusions { Bayesian inference for a nonhomogeneous Poisson process with an S-shaped mean value function is studied. In particular, we consider the model proposed by Ohba, et al, and its generalization to a class of gamma distribution growth 1 curves. Two Gibbs sampling approaches are proposed to compute the Bayes estimates of the s-expected 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.