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Imperfect debugging in software reliability: A Bayesian approach

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  • Aktekin, Tevfik
  • Caglar, Toros

Abstract

The objective of studying software reliability is to assist software engineers in understanding more of the probabilistic nature of software failures during the debugging stages and to construct reliability models. In this paper, we consider modeling of a multiplicative failure rate whose components are evolving stochastically over testing stages and discuss its Bayesian estimation. In doing so, we focus on the modeling of parameters such as the fault detection rate per fault and the number of faults. We discuss how the proposed model can account for “imperfect debugging” under certain conditions. We use actual inter-failure data to carry out inference on model parameters via Markov chain Monte Carlo methods and present additional insights from Bayesian analysis.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:ejores:v:227:y:2013:i:1:p:112-121
    DOI: 10.1016/j.ejor.2012.11.056
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    Cited by:

    1. Qing Tian & Chun-Wu Yeh & Chih-Chiang Fang, 2022. "Bayesian Decision Making of an Imperfect Debugging Software Reliability Growth Model with Consideration of Debuggers’ Learning and Negligence Factors," Mathematics, MDPI, vol. 10(10), pages 1-21, May.
    2. Qing Tian & Chih-Chiang Fang & Chun-Wu Yeh, 2022. "Software Release Assessment under Multiple Alternatives with Consideration of Debuggers’ Learning Rate and Imperfect Debugging Environment," Mathematics, MDPI, vol. 10(10), pages 1-24, May.
    3. Jørgen Vitting Andersen & Roy Cerqueti & Giulia Rotundo, 2017. "Rational expectations and stochastic systems," Documents de travail du Centre d'Economie de la Sorbonne 17060, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne, revised Oct 2019.
    4. Fernández, Arturo J., 2015. "Optimum attributes component test plans for k-out-of-n:F Weibull systems using prior information," European Journal of Operational Research, Elsevier, vol. 240(3), pages 688-696.
    5. Jorgen-Vitting Andersen & Roy Cerqueti & Jessica Riccioni, 2023. "Rational expectations as a tool for predicting failure of weighted k-out-of-n reliability systems," Annals of Operations Research, Springer, vol. 326(1), pages 295-316, July.
    6. 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.

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