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Software Reliability Model with Dependent Failures and SPRT

Author

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  • Da Hye Lee

    (Department of Computer Science and Statistics, Chosun University, 309 Pilmun-daero, Dong-gu, Gwangju 61452, Korea)

  • In Hong Chang

    (Department of Computer Science and Statistics, Chosun University, 309 Pilmun-daero, Dong-gu, Gwangju 61452, Korea)

  • Hoang Pham

    (Department of Industrial and Systems Engineering, Rutgers University, 96 Frelinghuysen Road, Piscataway, NJ 08855-8018, USA)

Abstract

Software reliability and quality are crucial in several fields. Related studies have focused on software reliability growth models (SRGMs). Herein, we propose a new SRGM that assumes interdependent software failures. We conduct experiments on real-world datasets to compare the goodness-of-fit of the proposed model with the results of previous nonhomogeneous Poisson process SRGMs using several evaluation criteria. In addition, we determine software reliability using Wald’s sequential probability ratio test (SPRT), which is more efficient than the classical hypothesis test (the latter requires substantially more data and time because the test is performed only after data collection is completed). The experimental results demonstrate the superiority of the proposed model and the effectiveness of the SPRT.

Suggested Citation

  • Da Hye Lee & In Hong Chang & Hoang Pham, 2020. "Software Reliability Model with Dependent Failures and SPRT," Mathematics, MDPI, vol. 8(8), pages 1-14, August.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:8:p:1366-:d:399290
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    References listed on IDEAS

    as
    1. Triet Pham & Hoang Pham, 2019. "A generalized software reliability model with stochastic fault-detection rate," Annals of Operations Research, Springer, vol. 277(1), pages 83-93, June.
    2. 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.
    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. Hoang Pham, 2006. "System Software Reliability," Springer Series in Reliability Engineering, Springer, number 978-1-84628-295-9, April.
    5. Subhashis Chatterjee & Ankur Shukla & Hoang Pham, 2019. "Modeling and analysis of software fault detectability and removability with time variant fault exposure ratio, fault removal efficiency, and change point," Journal of Risk and Reliability, , vol. 233(2), pages 246-256, April.
    6. Xuemei Zhang & Daniel R. Jeske & Hoang Pham, 2002. "Calibrating software reliability models when the test environment does not match the user environment," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 18(1), pages 87-99, January.
    7. Cadini, F. & Gioletta, A., 2016. "A Bayesian Monte Carlo-based algorithm for the estimation of small failure probabilities of systems affected by uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 153(C), pages 15-27.
    8. Shinji Inoue & Jun Ikeda & Shigeru Yamada, 2016. "Bivariate change-point modeling for software reliability assessment with uncertainty of testing-environment factor," Annals of Operations Research, Springer, vol. 244(1), pages 209-220, September.
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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. Dahye Lee & Inhong Chang & Hoang Pham, 2023. "Study of a New Software Reliability Growth Model under Uncertain Operating Environments and Dependent Failures," Mathematics, MDPI, vol. 11(18), pages 1-17, September.
    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.
    4. Aleksandr Kulikov & Pavel Ilyushin & Anton Loskutov & Konstantin Suslov & Sergey Filippov, 2022. "WSPRT Methods for Improving Power System Automation Devices in the Conditions of Distributed Generation Sources Operation," Energies, MDPI, vol. 15(22), pages 1-20, November.

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