An adaptive concatenated failure rate model for software reliability

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

8 Scopus Citations
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Author(s)

  • Dhaifalla Al-Mutairi
  • Yiping Chen
  • Nozer D. Singpurwalla

Detail(s)

Original languageEnglish
Pages (from-to)1150-1163
Journal / PublicationJournal of the American Statistical Association
Volume93
Issue number443
Publication statusPublished - Sept 1998
Externally publishedYes

Abstract

This article introduces a software reliability model whose concatenated failure rate function is motivated via considerations that reflect an engineer's knowledge about the stochastic nature of software failures. The model is adaptive (in a sense explained), has two parameters, and has characteristics that generalize those of existing models. A Bayesian approach for estimating the model parameters and for testing hypotheses about reliability growth is proposed. The prior distributions reflect structural considerations, and Markov chain Monte Carlo techniques are used to implement the approach.

Research Area(s)

  • Bayes factors, Gibbs sampling, Markov chain Monte Carlo, Point process, Reliability growth

Citation Format(s)

An adaptive concatenated failure rate model for software reliability. / Al-Mutairi, Dhaifalla; Chen, Yiping; Singpurwalla, Nozer D.
In: Journal of the American Statistical Association, Vol. 93, No. 443, 09.1998, p. 1150-1163.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review