IDEAS home Printed from https://ideas.repec.org/a/eee/apmaco/v402y2021ics0096300321001648.html
   My bibliography  Save this article

Sequential procedure for Software Reliability estimation

Author

Listed:
  • Zarzour, Nasir
  • Rekab, Kamel

Abstract

We use a sequential method to allocate software test cases among partitions of a software to minimize the expected loss incurred by the Bayes estimator of the overall software reliability. The Bayesian approach allows us to take advantage of the previous information obtained from testing. We will show that the myopic sampling scheme has advantages over the optimal fixed in terms of the expected loss incurred when the overall reliability is estimated by its Bayes estimator. Theoretical results and numerical are provided for the comparison. This myopic scheme shows a great promise in software reliability estimation.

Suggested Citation

  • Zarzour, Nasir & Rekab, Kamel, 2021. "Sequential procedure for Software Reliability estimation," Applied Mathematics and Computation, Elsevier, vol. 402(C).
    • Handle: RePEc:eee:apmaco:v:402:y:2021:i:c:s0096300321001648
    DOI: 10.1016/j.amc.2021.126116
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0096300321001648
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.amc.2021.126116?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

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


    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. Liu, Di & Wang, Shaoping & Zhang, Chao, 2022. "Reliability estimation from two types of accelerated testing data based on an artificial neural network supported Wiener process," Applied Mathematics and Computation, Elsevier, vol. 417(C).

    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:eee:apmaco:v:402:y:2021:i:c:s0096300321001648. 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: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/applied-mathematics-and-computation .

    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.