Generalised rule induction-based model for software fault prediction
by Ashutosh Mishra; Meenu Singla
International Journal of Reliability and Safety (IJRS), Vol. 15, No. 1/2, 2021

Abstract: Software fault predictions have great importance during maintenance and evolution of softwares. To upgrade the quality of the software, it is necessary to predict the faulty software modules. Previous research investigated was mainly focused on the binary classification of software class modules using different fault prediction techniques. However, much less work was done on the prediction of the number of faults. In this study, a descriptive technique called as Generalised Rule Induction (GRI) associations rule mining is proposed to identify the number of faults in the faulty class module. The proposed technique is implemented for five releases of open source Apache Ant project which is taken from PROMISE repository. The results show that the generated rules for the class modules containing single fault achieves better accuracy with fewer rules.

Online publication date: Mon, 13-Dec-2021

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
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:

Username:        Password:          Forgotten your 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 subs@inderscience.com