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Software reliability growth models with normal failure time distributions

Author

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  • Okamura, Hiroyuki
  • Dohi, Tadashi
  • Osaki, Shunji

Abstract

This paper proposes software reliability growth models (SRGM) where the software failure time follows a normal distribution. The proposed model is mathematically tractable and has sufficient ability of fitting to the software failure data. In particular, we consider the parameter estimation algorithm for the SRGM with normal distribution. The developed algorithm is based on an EM (expectation-maximization) algorithm and is quite simple for implementation as software application. Numerical experiment is devoted to investigating the fitting ability of the SRGMs with normal distribution through 16 types of failure time data collected in real software projects.

Suggested Citation

  • Okamura, Hiroyuki & Dohi, Tadashi & Osaki, Shunji, 2013. "Software reliability growth models with normal failure time distributions," Reliability Engineering and System Safety, Elsevier, vol. 116(C), pages 135-141.
    • Handle: RePEc:eee:reensy:v:116:y:2013:i:c:p:135-141
    DOI: 10.1016/j.ress.2012.02.002
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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. Li, Dongmin & Hu, Qingpei & Wang, Lujia & Yu, Dan, 2019. "Statistical inference for Mt/G/Infinity queueing systems under incomplete observations," European Journal of Operational Research, Elsevier, vol. 279(3), pages 882-901.
    3. Franko, Mitja & Nagode, Marko, 2015. "Probability density function of the equivalent stress amplitude using statistical transformation," Reliability Engineering and System Safety, Elsevier, vol. 134(C), pages 118-125.
    4. Yaghoobi, Tahere, 2020. "Parameter optimization of software reliability models using improved differential evolution algorithm," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 177(C), pages 46-62.
    5. Wang, Jinyong & Zhang, Ce, 2018. "Software reliability prediction using a deep learning model based on the RNN encoder–decoder," Reliability Engineering and System Safety, Elsevier, vol. 170(C), pages 73-82.
    6. Awad, Mahmoud, 2016. "Economic allocation of reliability growth testing using Weibull distributions," Reliability Engineering and System Safety, Elsevier, vol. 152(C), pages 273-280.
    7. 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.
    8. Hiroyuki Okamura & Tadashi Dohi, 2016. "Phase-type software reliability model: parameter estimation algorithms with grouped data," Annals of Operations Research, Springer, vol. 244(1), pages 177-208, September.
    9. Hiroyuki Okamura & Tadashi Dohi, 2021. "Application of EM Algorithm to NHPP-Based Software Reliability Assessment with Generalized Failure Count Data," Mathematics, MDPI, vol. 9(9), pages 1-18, April.

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