A metric to detect fault-prone software modules using text filtering Online publication date: Sat, 20-Sep-2014
by Osamu Mizuno; Hideaki Hata
International Journal of Reliability and Safety (IJRS), Vol. 7, No. 1, 2013
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.
Online publication date: Sat, 20-Sep-2014
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Reliability and Safety (IJRS):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email firstname.lastname@example.org