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A study of software reliability growth from the perspective of learning effects

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  • Chiu, Kuei-Chen
  • Huang, Yeu-Shiang
  • Lee, Tzai-Zang

Abstract

For the last three decades, reliability growth has been studied to predict software reliability in the testing/debugging phase. Most of the models developed were based on the non-homogeneous Poisson process (NHPP), and S-shaped type or exponential-shaped type of behavior is usually assumed. Unfortunately, such models may be suitable only for particular software failure data, thus narrowing the scope of applications. Therefore, from the perspective of learning effects that can influence the process of software reliability growth, we considered that efficiency in testing/debugging concerned not only the ability of the testing staff but also the learning effect that comes from inspecting the testing/debugging codes. The proposed approach can reasonably describe the S-shaped and exponential-shaped types of behaviors simultaneously, and the results in the experiment show good fit. A comparative analysis to evaluate the effectiveness for the proposed model and other software failure models was also performed. Finally, an optimal software release policy is suggested.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:reensy:v:93:y:2008:i:10:p:1410-1421
    DOI: 10.1016/j.ress.2007.11.004
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    References listed on IDEAS

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    1. Hu, Q.P. & Xie, M. & Ng, S.H. & Levitin, G., 2007. "Robust recurrent neural network modeling for software fault detection and correction prediction," Reliability Engineering and System Safety, Elsevier, vol. 92(3), pages 332-340.
    2. Pham, Hoang, 2003. "Software reliability and cost models: Perspectives, comparison, and practice," European Journal of Operational Research, Elsevier, vol. 149(3), pages 475-489, September.
    3. 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. 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. 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.
    3. 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.
    4. Utkin, Lev V. & Coolen, Frank P.A., 2018. "A robust weighted SVR-based software reliability growth model," Reliability Engineering and System Safety, Elsevier, vol. 176(C), pages 93-101.
    5. Chih-Chiang Fang & Chun-Wu Yeh, 2016. "Effective confidence interval estimation of fault-detection process of software reliability growth models," International Journal of Systems Science, Taylor & Francis Journals, vol. 47(12), pages 2878-2892, September.
    6. 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.
    7. Tahere Yaghoobi & Man-Fai Leung, 2023. "Modeling Software Reliability with Learning and Fatigue," Mathematics, MDPI, vol. 11(16), pages 1-20, August.
    8. 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.
    9. Tabassum Naz Sindhu & Sadia Anwar & Marwa K. H. Hassan & Showkat Ahmad Lone & Tahani A. Abushal & Anum Shafiq, 2023. "An Analysis of the New Reliability Model Based on Bathtub-Shaped Failure Rate Distribution with Application to Failure Data," Mathematics, MDPI, vol. 11(4), pages 1-18, February.
    10. Gaver, Donald P. & Jacobs, Patricia A., 2014. "Reliability growth by failure mode removal," Reliability Engineering and System Safety, Elsevier, vol. 130(C), pages 27-32.
    11. M Xie & X Li & S H Ng, 2011. "Risk-based software release policy under parameter uncertainty," Journal of Risk and Reliability, , vol. 225(1), pages 42-49, March.

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