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A Bayesian hidden Markov model for imperfect debugging

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  • Pievatolo, Antonio
  • Ruggeri, Fabrizio
  • Soyer, Refik

Abstract

In this paper we present a new model to describe software failures from a debugging process. Our model allows for the imperfect debugging scenario by considering potential introduction of new bugs to the software during the development phase. Since the introduction of bugs is an unobservable process, latent variables are introduced to incorporate this property via a hidden Markov model. We develop a Bayesian analysis of the model and discuss its extensions. We also consider how to infer the unknown number of states of the hidden Markov model. The model and the Bayesian analysis are implemented to actual software failure data.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:reensy:v:103:y:2012:i:c:p:11-21
    DOI: 10.1016/j.ress.2012.03.003
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. Yang, Bo & Li, Xiang & Xie, Min & Tan, Feng, 2010. "A generic data-driven software reliability model with model mining technique," Reliability Engineering and System Safety, Elsevier, vol. 95(6), pages 671-678.
    4. Chiu, Kuei-Chen & Huang, Yeu-Shiang & Lee, Tzai-Zang, 2008. "A study of software reliability growth from the perspective of learning effects," Reliability Engineering and System Safety, Elsevier, vol. 93(10), pages 1410-1421.
    5. Chib S. & Jeliazkov I., 2001. "Marginal Likelihood From the Metropolis-Hastings Output," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 270-281, March.
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    Citations

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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. Min Xie & Chengjie Xiong & Szu-Hui Ng, 2014. "A study of N-version programming and its impact on software availability," International Journal of Systems Science, Taylor & Francis Journals, vol. 45(10), pages 2145-2157, October.
    3. Chen, Gaige & Chen, Jinglong & Zi, Yanyang & Miao, Huihui, 2017. "Hyper-parameter optimization based nonlinear multistate deterioration modeling for deterioration level assessment and remaining useful life prognostics," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 517-526.
    4. Ceren Eda Can & Gul Ergun & Refik Soyer, 2022. "Bayesian Analysis of Proportions via a Hidden Markov Model," Methodology and Computing in Applied Probability, Springer, vol. 24(4), pages 3121-3139, December.
    5. 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.
    6. Peng, R. & Li, Y.F. & Zhang, W.J. & Hu, Q.P., 2014. "Testing effort dependent software reliability model for imperfect debugging process considering both detection and correction," Reliability Engineering and System Safety, Elsevier, vol. 126(C), pages 37-43.
    7. Moghaddass, Ramin & Zuo, Ming J., 2012. "A parameter estimation method for a condition-monitored device under multi-state deterioration," Reliability Engineering and System Safety, Elsevier, vol. 106(C), pages 94-103.
    8. Wang, Jinyong & Wu, Zhibo, 2016. "Study of the nonlinear imperfect software debugging model," Reliability Engineering and System Safety, Elsevier, vol. 153(C), pages 180-192.

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