Bayesian Analysis ware Reliability Growth Model with Negative Binomial Information 


Vol. 7,  No. 3, pp. 852-861, Mar.  2000
10.3745/KIPSTE.2000.7.3.852


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  Abstract

Software reliability growth models are used in testing stages of software development to model the error content and time intervals between software failures. In this paper, using priors for the number of fault with the negative binomial distribution and the error rate with gamma distribution, Bayesian inference and model selection method for Jelinski- Moranda and Goel-Okumoto and Schick-Wolverton models in software reliability. For model selection, we explored the sum of the relative error, Braun statistic and median variation. In Bayesian computation process, we could avoid the multiple integration by the use of Gibbs sampling, which is a kind of Markov Chain Monte Carlo method to compute the posterior distribution. Using simulated data, Bayesian inference and model selection is studied.

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[IEEE Style]

H. C. Kim, J. G. Park, B. S. Lee, "Bayesian Analysis ware Reliability Growth Model with Negative Binomial Information," The Transactions of the Korea Information Processing Society (1994 ~ 2000), vol. 7, no. 3, pp. 852-861, 2000. DOI: 10.3745/KIPSTE.2000.7.3.852.

[ACM Style]

Hee Cheul Kim, Jong Goo Park, and Byoung Soo Lee. 2000. Bayesian Analysis ware Reliability Growth Model with Negative Binomial Information. The Transactions of the Korea Information Processing Society (1994 ~ 2000), 7, 3, (2000), 852-861. DOI: 10.3745/KIPSTE.2000.7.3.852.

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