IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i18p3810-d1233488.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/18/3810/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/11/18/3810/
    Download Restriction: no
    ---><---

    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Kwang Yoon Song & Youn Su Kim & Hoang Pham & In Hong Chang, 2024. "A Software Reliability Model Considering a Scale Parameter of the Uncertainty and a New Criterion," Mathematics, MDPI, vol. 12(11), pages 1-14, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. 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.
    3. Wang, Peipei & Zheng, Xinqi & Ai, Gang & Liu, Dongya & Zhu, Bangren, 2020. "Time series prediction for the epidemic trends of COVID-19 using the improved LSTM deep learning method: Case studies in Russia, Peru and Iran," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    4. Liviu Adrian COTFAS & Andreea DIOSTEANU, 2010. "Software Reliability in Semantic Web Service Composition Applications," Informatica Economica, Academy of Economic Studies - Bucharest, Romania, vol. 14(4), pages 48-56.
    5. 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.
    6. Kwang Yoon Song & In Hong Chang & Hoang Pham, 2019. "A Testing Coverage Model Based on NHPP Software Reliability Considering the Software Operating Environment and the Sensitivity Analysis," Mathematics, MDPI, vol. 7(5), pages 1-21, May.
    7. Xu, Zhaoyi & Saleh, Joseph Homer, 2021. "Machine learning for reliability engineering and safety applications: Review of current status and future opportunities," Reliability Engineering and System Safety, Elsevier, vol. 211(C).
    8. Subhashis Chatterjee & Shobhit Nigam & Jeetendra Bahadur Singh & Lakshmi Narayan Upadhyaya, 2012. "Effect of change point and imperfect debugging in software reliability and its optimal release policy," Mathematical and Computer Modelling of Dynamical Systems, Taylor & Francis Journals, vol. 18(5), pages 539-551, March.
    9. Wang, Jinyong & Wu, Zhibo, 2016. "Study of the nonlinear imperfect software debugging model," Reliability Engineering and System Safety, Elsevier, vol. 153(C), pages 180-192.
    10. 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.
    11. Subhashis Chatterjee & Jeetendra B. Singh & Arunava Roy, 2015. "A structure-based software reliability allocation using fuzzy analytic hierarchy process," International Journal of Systems Science, Taylor & Francis Journals, vol. 46(3), pages 513-525, February.
    12. Gaurav Mishra & P. K. Kapur & Anu G. Aggarwal, 2023. "A generalized multi-upgradation SRGM considering uncertainty of random field operating environments," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(1), pages 328-336, March.
    13. Bistouni, Fathollah & Jahanshahi, Mohsen, 2017. "Remove and contraction: A novel method for calculating the reliability of Ethernet ring mesh networks," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 362-375.
    14. Sadiya & Mangey Ram & Akshay Kumar, 2022. "A New Approach to Compute System Reliability with Three-Serially Linked Modules," Mathematics, MDPI, vol. 11(1), pages 1-18, December.
    15. 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.
    16. Anshul Tickoo & P. K. Kapur & A. K. Shrivastava & Sunil K. Khatri, 2016. "Testing effort based modeling to determine optimal release and patching time of software," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 7(4), pages 427-434, December.
    17. Hoang Pham, 2020. "Estimating the COVID-19 Death Toll by Considering the Time-Dependent Effects of Various Pandemic Restrictions," Mathematics, MDPI, vol. 8(9), pages 1-12, September.
    18. Hoang Pham, 2019. "A New Criterion for Model Selection," Mathematics, MDPI, vol. 7(12), pages 1-12, December.
    19. Yi-Ting Chen & Edward W. Sun & Yi-Bing Lin, 2019. "Coherent quality management for big data systems: a dynamic approach for stochastic time consistency," Annals of Operations Research, Springer, vol. 277(1), pages 3-32, June.
    20. Tahere Yaghoobi & Man-Fai Leung, 2023. "Modeling Software Reliability with Learning and Fatigue," Mathematics, MDPI, vol. 11(16), pages 1-20, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:11:y:2023:i:18:p:3810-:d:1233488. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.