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Study of a New Software Reliability Growth Model under Uncertain Operating Environments and Dependent Failures

Author

Listed:
  • Dahye Lee

    (Department of Computer Science and Statistics, Chosun University, 146 Chosundae-gil, Dong-gu, Gwangju 61452, Republic of Korea)

  • Inhong Chang

    (Department of Computer Science and Statistics, Chosun University, 146 Chosundae-gil, Dong-gu, Gwangju 61452, Republic of Korea)

  • Hoang Pham

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

Abstract

The coronavirus disease (COVID-19) outbreak has prompted various industries to embark on digital transformation efforts, with software playing a critical role. Ensuring the reliability of software is of the utmost importance given its widespread use across multiple industries. For example, software has extensive applications in areas such as transportation, aviation, and military systems, where reliability problems can result in personal injuries and significant financial losses. Numerous studies have focused on software reliability. In particular, the software reliability growth model has served as a prominent tool for measuring software reliability. Previous studies have often assumed that the testing environment is representative of the operating environment and that software failures occur independently. However, the testing and operating environments can differ, and software failures can sometimes occur dependently. In this study, we propose a new model that assumes uncertain operating environments and dependent failures. In other words, the model proposed in this study takes into account a wider range of environments. The numerical examples in this study demonstrate that the goodness of fit of the new model is significantly better than that of the existing SRGM. Additionally, we show the utilization of the sequential probability ratio test (SPRT) based on the new model to assess the reliability of the dataset.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:18:p:3810-:d:1233488
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    References listed on IDEAS

    as
    1. 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.
    2. Mengmeng Zhu, 2022. "A new framework of complex system reliability with imperfect maintenance policy," Annals of Operations Research, Springer, vol. 312(1), pages 553-579, May.
    3. Hoang Pham, 2006. "System Software Reliability," Springer Series in Reliability Engineering, Springer, number 978-1-84628-295-9, March.
    4. 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.
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