Does software reliability growth behavior follow a non-homogeneous Poisson process
Information and Software Technology
A Bayesian Inference Tool for NHPP-Based Software Reliability Assessment
FGIT '09 Proceedings of the 1st International Conference on Future Generation Information Technology
Wavelet-based approach for estimating software reliability
ISSRE'09 Proceedings of the 20th IEEE international conference on software reliability engineering
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In this paper, we present a variational Bayesian (VB) approach to computing the interval estimates for nonhomogeneous Poisson process (NHPP) software reliability models. This approach is an approximate method that can produce analytically tractable posterior distributions. We present simple iterative algorithms to compute the approximate posterior distributions for the parameters of the gamma-type NHPP-based software reliability model using either individual failure time data or grouped data. In numerical examples, the accuracy of this VB approach is compared with the interval estimates based on conventional Bayesian approaches, i.e., Laplace approximation, Markov chain Monte Carlo (MCMC) method, and numerical integration. The proposed VB approach provides almost the same accuracy as MCMC, while its computational burden is much lower.