IDEAS home Printed from https://ideas.repec.org/a/sae/risrel/v240y2026i3p944-962.html

A software reliability model with Weibull distributed fault removal efficiency for open source big data systems

Author

Listed:
  • Jinyong Wang
  • Pengda Li
  • Boxuan Li

Abstract

The widespread integration of big data technologies into daily life has heightened the demand for reliable software systems, yet research on open source software reliability lags behind that of closed source software, whose theoretical framework is already mature. Conventional software reliability growth models fail to address the unique attributes of open source big data systems, creating an urgent need for a specialized reliability assessment tool. To fill this gap, this paper proposes an imperfect debugging reliability model that considers big data technology characteristics and captures the complex dynamics of fault removal efficiency using a Weibull distribution. Rigorous experimental validation demonstrates the model’s exceptional fitting accuracy and predictive power. By enabling effective and precise reliability evaluation of open source big data system software, this work not only advances the theoretical frontier of software reliability but also provides actionable insights for optimizing software development and testing processes.

Suggested Citation

  • Jinyong Wang & Pengda Li & Boxuan Li, 2026. "A software reliability model with Weibull distributed fault removal efficiency for open source big data systems," Journal of Risk and Reliability, , vol. 240(3), pages 944-962, June.
    • Handle: RePEc:sae:risrel:v:240:y:2026:i:3:p:944-962
    DOI: 10.1177/1748006X261423787
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/1748006X261423787
    Download Restriction: no
    File URL: https://libkey.io/10.1177/1748006X261423787?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:sae:risrel:v:240:y:2026:i:3:p:944-962. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: SAGE Publications (email available below). General contact details of provider: .

    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.