A package-based clustering approach to enhance the accuracy and performance of software defect prediction Online publication date: Tue, 03-Oct-2017
by Rayhanul Islam; Kazi Sakib
International Journal of Software Engineering, Technology and Applications (IJSETA), Vol. 2, No. 1, 2017
Abstract: To enhance the accuracy and performance, software defect prediction models considering clustering of dataset combine related and similar features to improve the learning process of the model. Here, a clustering approach named package-based clustering has been proposed to group the similar and related parts of software using object oriented classes' relationships and similarities. To segregate software into clusters, it performs textual analysis to identify all object-oriented classes from source codes. Then it uses package information of each class to divide those into clusters. To analyse the performance of the proposed algorithm, linear regression model is used, which learns from clusters of related and similar classes. The experiment has been conducted on eight releases of two open source software, which are Xalan and Ant, and results show that the proposed technique outperforms the existing clustering algorithms those are BorderFlow and the entire system.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
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 Software Engineering, Technology and Applications (IJSETA):
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 subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook