IDEAS home Printed from https://ideas.repec.org/a/ids/ijrsaf/v7y2013i1p17-31.html
   My bibliography  Save this article

A metric to detect fault-prone software modules using text filtering

Author

Listed:
  • Osamu Mizuno
  • Hideaki Hata

Abstract

Machine learning approaches have been widely used for fault-prone module detection. Introduction of machine learning approaches induces development of new software metrics for fault-prone module detection. We have proposed an approach to detect fault-prone modules using the spam-filtering technique. To use our approach in the conventional fault-prone module prediction approaches, we construct a metric from the output of spam-filtering based approach. Using our new metric, we conducted an experiment to show the effect of new metric. The result suggested that use of new metric as well as conventional metrics is effective for accuracy of fault-prone module prediction.

Suggested Citation

  • Osamu Mizuno & Hideaki Hata, 2013. "A metric to detect fault-prone software modules using text filtering," International Journal of Reliability and Safety, Inderscience Enterprises Ltd, vol. 7(1), pages 17-31.
  • Handle: RePEc:ids:ijrsaf:v:7:y:2013:i:1:p:17-31
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=55822
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

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

    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:ids:ijrsaf:v:7:y:2013:i:1:p:17-31. 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: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=98 .

    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.