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NHPP models with Markov switching for software reliability

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  • Ravishanker, Nalini
  • Liu, Zhaohui
  • Ray, Bonnie K.

Abstract

We describe the use of a latent Markov process governing the parameters of a nonhomogeneous Poisson process (NHPP) model for characterizing the software development defect discovery process. Use of a Markov switching process allows us to characterize non-smooth variations in the rate at which defects are found, better reflecting the industrial software development environment in practice. Additionally, we propose a multivariate model for characterizing changes in the distribution of defect types that are found over time, conditional on the total number of defects. A latent Markov chain governs the evolution of probabilities of the different types. Bayesian methods via Markov chain Monte Carlo facilitate inference. We illustrate the efficacy of the methods using simulated data, then apply them to model reliability growth in a large operating system software component-based on defects discovered during the system testing phase of development.

Suggested Citation

  • Ravishanker, Nalini & Liu, Zhaohui & Ray, Bonnie K., 2008. "NHPP models with Markov switching for software reliability," Computational Statistics & Data Analysis, Elsevier, vol. 52(8), pages 3988-3999, April.
    • Handle: RePEc:eee:csdana:v:52:y:2008:i:8:p:3988-3999
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    References listed on IDEAS

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    1. Antonio Pievatolo & Fabrizio Ruggeri, 2004. "Bayesian reliability analysis of complex repairable systems," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 20(3), pages 253-264, July.
    2. Chib, Siddhartha, 1996. "Calculating posterior distributions and modal estimates in Markov mixture models," Journal of Econometrics, Elsevier, vol. 75(1), pages 79-97, November.
    3. Zhaohui Liu & Nalini Ravishanker & Bonnie K. Ray, 2005. "NHPP models for categorized software defects," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 21(6), pages 509-524, November.
    4. Scott S. L., 2002. "Bayesian Methods for Hidden Markov Models: Recursive Computing in the 21st Century," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 337-351, March.
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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.
    2. Cong Lin & Lirong Cui & David Coit & Min Lv, 2017. "An approximation method for evaluating the reliability of a dynamic k-out-of-n:F system subjected to cyclic alternating operation conditions," Journal of Risk and Reliability, , vol. 231(2), pages 109-120, April.
    3. Aktekin, Tevfik & Caglar, Toros, 2013. "Imperfect debugging in software reliability: A Bayesian approach," European Journal of Operational Research, Elsevier, vol. 227(1), pages 112-121.

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