A Bayesian change-point analysis for software reliability models

Seungmin Nam, Ji Hwan Cha, Sinsup Cho

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


In most software reliability models which utilize the nonhomogeneous Poisson process (NHPP), the intensity function for the counting process is usually assumed to be continuous and monotone. However, on account of various practical reasons, there may exist some change points in the intensity function and thus the assumption of continuous and monotone intensity function may be unrealistic in many real situations. In this article, the Bayesian change-point approach using beta-mixtures for modeling the intensity function with possible change points is proposed. The hidden Markov model with non constant transition probabilities is applied to the beta-mixture for detecting the change points of the parameters. The estimation and interpretation of the model is illustrated using the Naval Tactical Data System (NTDS) data. The proposed change point model will be also compared with the competing models via marginal likelihood. It can be seen that the proposed model has the highest marginal likelihood and outperforms the competing models.

Original languageEnglish
Pages (from-to)1855-1869
Number of pages15
JournalCommunications in Statistics: Simulation and Computation
Issue number9
StatePublished - Nov 2008

Bibliographical note

Funding Information:
The result of Sinsup Cho is supported by the Korea Research Foundation Grant (KRF-2005-070-C00022) funded by the Korean Government (MOHEAD). This work was supported by the Korea Science and Engineering Foundation (KOSEF) grant funded by the Korean government (MOST) (No. R01-2008-000-10957-0). The authors are grateful to I. K. Yeo for suggesting the critical idea of the manuscript. The authors also appreciate the referee’s suggestions and comments which improved the presentation of the article.


  • Bayes factor
  • Change point
  • Intensity function
  • MCMC
  • NHPP
  • Software reliability


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