The Bayesian Inference for Software Reliability Models Based on NHPP 


Vol. 9,  No. 3, pp. 389-398, Jun.  2002
10.3745/KIPSTD.2002.9.3.389


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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 betwewn software failures. This paper presents a stochastic model for the software failure phenomenon based on a nonhomogeneous Poisson process (NHPP) and performs Bayesian inference using prior information. The failure process is analyzed to develop a suitable mean value function for the NHPP; expressions are given for several performance measure. Actual software failure data are compared with several model on the constant reflecting the quality of testing. The performance measures and parametric inferences of the suggested models using Rayleigh distribution and Laplace distribution are discussed. The results of the suggested models are applied to real software failure data and compared with Goel model. Tools of parameter point inference and 95% credible intereval was used method of Gibbs sampling. In this paper, model selection using the sum of the squared errors was employed. The numerical example by NTDS data was illustrated.

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[IEEE Style]

S. S. Lee, H. C. Kim, Y. J. Song, "The Bayesian Inference for Software Reliability Models Based on NHPP," The KIPS Transactions:PartD, vol. 9, no. 3, pp. 389-398, 2002. DOI: 10.3745/KIPSTD.2002.9.3.389.

[ACM Style]

Sang Sik Lee, Hee Cheul Kim, and Young Jae Song. 2002. The Bayesian Inference for Software Reliability Models Based on NHPP. The KIPS Transactions:PartD, 9, 3, (2002), 389-398. DOI: 10.3745/KIPSTD.2002.9.3.389.

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