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

Osamu Mizuno and Hideaki Hata

International Journal of Reliability and Safety, 2013, vol. 7, issue 1, 17-31

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.

Keywords: software modules; fault-prone modules; fault detection; machine learning; software metrics; text filtering; spam filtering. (search for similar items in EconPapers)
Date: 2013
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=55822 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijrsaf:v:7:y:2013:i:1:p:17-31

Access Statistics for this article

More articles in International Journal of Reliability and Safety from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().