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A Markov modulated Poisson model for software reliability

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  • Landon, Joshua
  • Özekici, Süleyman
  • Soyer, Refik

Abstract

In this paper, we consider a latent Markov process governing the intensity rate of a Poisson process model for software failures. The latent process enables us to infer performance of the debugging operations over time and allows us to deal with the imperfect debugging scenario. We develop the Bayesian inference for the model and also introduce a method to infer the unknown dimension of the Markov process. We illustrate the implementation of our model and the Bayesian approach by using actual software failure data.

Suggested Citation

  • Landon, Joshua & Özekici, Süleyman & Soyer, Refik, 2013. "A Markov modulated Poisson model for software reliability," European Journal of Operational Research, Elsevier, vol. 229(2), pages 404-410.
    • Handle: RePEc:eee:ejores:v:229:y:2013:i:2:p:404-410
    DOI: 10.1016/j.ejor.2013.03.014
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    References listed on IDEAS

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    1. Ozekici, S. & Soyer, R., 2003. "Reliability of software with an operational profile," European Journal of Operational Research, Elsevier, vol. 149(2), pages 459-474, September.
    2. Çekyay, B. & Özekici, S., 2010. "Mean time to failure and availability of semi-Markov missions with maximal repair," European Journal of Operational Research, Elsevier, vol. 207(3), pages 1442-1454, December.
    3. Paul Fearnhead & Chris Sherlock, 2006. "An exact Gibbs sampler for the Markov‐modulated Poisson process," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(5), pages 767-784, November.
    4. 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.
    5. Arifoglu, Kenan & Özekici, Süleyman, 2010. "Optimal policies for inventory systems with finite capacity and partially observed Markov-modulated demand and supply processes," European Journal of Operational Research, Elsevier, vol. 204(3), pages 421-438, August.
    6. S. Özekici & R. Soyer, 2006. "Semi-Markov modulated Poisson process: probabilistic and statistical analysis," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 64(1), pages 125-144, August.
    7. Pham, Hoang & Zhang, Xuemei, 2003. "NHPP software reliability and cost models with testing coverage," European Journal of Operational Research, Elsevier, vol. 145(2), pages 443-454, March.
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    Cited by:

    1. Ramírez-Cobo, Pepa, 2017. "Findings about the two-state BMMPP for modeling point processes in reliability and queueing systems," DES - Working Papers. Statistics and Econometrics. WS 24622, Universidad Carlos III de Madrid. Departamento de Estadística.
    2. Gámiz, María Luz & Limnios, Nikolaos & Segovia-García, María del Carmen, 2023. "Hidden markov models in reliability and maintenance," European Journal of Operational Research, Elsevier, vol. 304(3), pages 1242-1255.
    3. Zhang, Nan & Fouladirad, Mitra & Barros, Anne, 2019. "Reliability-based measures and prognostic analysis of a K-out-of-N system in a random environment," European Journal of Operational Research, Elsevier, vol. 272(3), pages 1120-1131.
    4. Avanzi, Benjamin & Taylor, Greg & Wong, Bernard & Xian, Alan, 2021. "Modelling and understanding count processes through a Markov-modulated non-homogeneous Poisson process framework," European Journal of Operational Research, Elsevier, vol. 290(1), pages 177-195.
    5. Benjamin Avanzi & Greg Taylor & Bernard Wong & Alan Xian, 2020. "Modelling and understanding count processes through a Markov-modulated non-homogeneous Poisson process framework," Papers 2003.13888, arXiv.org, revised May 2020.
    6. Yera, Yoel G. & Lillo, Rosa E. & Ramírez-Cobo, Pepa, 2019. "Fitting procedure for the two-state Batch Markov modulated Poisson process," European Journal of Operational Research, Elsevier, vol. 279(1), pages 79-92.
    7. Yera, Yoel G. & Lillo, Rosa E. & Nielsen, Bo F. & Ramírez-Cobo, Pepa & Ruggeri, Fabrizio, 2021. "A bivariate two-state Markov modulated Poisson process for failure modeling," Reliability Engineering and System Safety, Elsevier, vol. 208(C).

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