IDEAS home Printed from https://ideas.repec.org/h/spr/ssrchp/978-3-031-05347-4_12.html
   My bibliography  Save this book chapter

Software Reliability Models and Multi-attribute Utility Function Based Strategic Decision for Release Time Optimization

In: Predictive Analytics in System Reliability

Author

Listed:
  • Vishal Pradhan

    (ABV-Indian Institute of Information Technology and Management Gwalior)

  • Joydip Dhar

    (ABV-Indian Institute of Information Technology and Management Gwalior)

  • Ajay Kumar

    (ABV-Indian Institute of Information Technology and Management Gwalior)

Abstract

The software industry is working hard to keep up with these rapid changes by devising methods to increase the pace of their work without compromising software quality and reliability. Various factors, such as the testing environment, testing strategy, and resource allocation, can influence the optimal release time. The choice of whether or not to release a software product would become much more complicated and significant. When a software developer, clients, or end-users face significant potential financial losses, a decision has strategic significance. A software release decision is a trade-off between early release to take advantage of an earlier market launch and product release deferral to ensure reliability. If a software product is released too soon, the software developer must pay for post-release costs to correct bugs. To decide the best software release time, two attributes, reliability and cost, must be combined. This study discusses a realistic approach to determining when to stop software testing that considers reliability and cost. A multi-attribute utility theory-based proposed decision model is analyzed on various separate weighted combinations of utility functions.

Suggested Citation

  • Vishal Pradhan & Joydip Dhar & Ajay Kumar, 2023. "Software Reliability Models and Multi-attribute Utility Function Based Strategic Decision for Release Time Optimization," Springer Series in Reliability Engineering, in: Vijay Kumar & Hoang Pham (ed.), Predictive Analytics in System Reliability, pages 175-190, Springer.
  • Handle: RePEc:spr:ssrchp:978-3-031-05347-4_12
    DOI: 10.1007/978-3-031-05347-4_12
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    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:spr:ssrchp:978-3-031-05347-4_12. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.