Bayes inference for S-shaped software-reliability growth models

Author

Kuo, Lynn ; Lee, Jae Chang ; Choi, Kiheon ; Yang, Tae Young

Author_Institution

Dept. of Stat., Connecticut Univ., Storrs, CT, USA

Volume

46

Issue

1

fYear

1997

fDate

3/1/1997 12:00:00 AM

Firstpage

76

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

Keywords

Bayes methods; gamma distribution; inference mechanisms; software reliability; stochastic processes; Bayes estimates; Bayes inference; Gibbs sampling approaches; Metropolis within Gibbs algorithm; S-shaped mean value function; S-shaped software-reliability growth models; current system reliability; data augmentation; errors remaining; gamma distribution growth curves; model selection; nonhomogeneous Poisson process; posterior Bayes factor; stochastic substitution algorithm; Computational modeling; Inference algorithms; Reliability; Sampling methods; Shape; Software algorithms; Software testing; Statistical distributions; Statistics; Stochastic processes;

fLanguage

English

Journal_Title

Reliability, IEEE Transactions on

Publisher

ieee

ISSN

0018-9529

Type

jour

DOI

10.1109/24.589931

Filename

589931