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Bayesian inference for a software reliability model using metrics information

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  • Wiper, Michael Peter
  • Rodríguez Bernal, María Teresa

Abstract

In this paper, we are concerned with predicting the number of faults N and the time to next failure of a piece of software. Information in the form of software metrics data is used to estimate the prior distribution of N via a Poisson regression model. Given failure time data, and a well known model for software failures, we show how to sample the posterior distribution using Gibbs sampling, as implemented in the package "WinBugs". The approach is illustrated with a practical example.

Suggested Citation

  • Wiper, Michael Peter & Rodríguez Bernal, María Teresa, 2001. "Bayesian inference for a software reliability model using metrics information," DES - Working Papers. Statistics and Econometrics. WS ws012014, Universidad Carlos III de Madrid. Departamento de Estadística.
    • Handle: RePEc:cte:wsrepe:ws012014
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    References listed on IDEAS

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    1. W. R. Gilks & P. Wild, 1992. "Adaptive Rejection Sampling for Gibbs Sampling," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 41(2), pages 337-348, June.
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    Cited by:

    1. Pievatolo, Antonio & Ruggeri, Fabrizio & Soyer, Refik, 2012. "A Bayesian hidden Markov model for imperfect debugging," Reliability Engineering and System Safety, Elsevier, vol. 103(C), pages 11-21.

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