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Effective confidence interval estimation of fault-detection process of software reliability growth models

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  • Chih-Chiang Fang
  • Chun-Wu Yeh

Abstract

The quantitative evaluation of software reliability growth model is frequently accompanied by its confidence interval of fault detection. It provides helpful information to software developers and testers when undertaking software development and software quality control. However, the explanation of the variance estimation of software fault detection is not transparent in previous studies, and it influences the deduction of confidence interval about the mean value function that the current study addresses. Software engineers in such a case cannot evaluate the potential hazard based on the stochasticity of mean value function, and this might reduce the practicability of the estimation. Hence, stochastic differential equations are utilised for confidence interval estimation of the software fault-detection process. The proposed model is estimated and validated using real data-sets to show its flexibility.

Suggested Citation

  • 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.
    • Handle: RePEc:taf:tsysxx:v:47:y:2016:i:12:p:2878-2892
    DOI: 10.1080/00207721.2015.1036474
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    References listed on IDEAS

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    1. Tamura, Yoshinobu & Yamada, Shigeru, 2006. "A flexible stochastic differential equation model in distributed development environment," European Journal of Operational Research, Elsevier, vol. 168(1), pages 143-152, January.
    2. P.K. Kapur & Hoang Pham & Udayan Chanda & Vijay Kumar, 2013. "Optimal allocation of testing effort during testing and debugging phases: a control theoretic approach," International Journal of Systems Science, Taylor & Francis Journals, vol. 44(9), pages 1639-1650.
    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.
    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.
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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. Subhashis Chatterjee & Ankur Shukla, 2016. "Change point–based software reliability model under imperfect debugging with revised concept of fault dependency," Journal of Risk and Reliability, , vol. 230(6), pages 579-597, December.
    3. 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.

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