A novel framework for labelling duplicate and non-duplicate bugs Online publication date: Mon, 19-Jun-2023
by Kulbhushan Bansal; Sunesh Malik; Manju Rohil; Harish Rohil
International Journal of Intelligent Systems Technologies and Applications (IJISTA), Vol. 21, No. 2, 2023
Abstract: Bug handling is an essential part in the software development life cycle. It can be very cumbersome, tedious and error-prone due to the complexity and size of software projects and teams. Duplicate bugs make the bug handling process even more tedious. In this paper, binary duplicate detection and ranking-based duplicate detection mechanisms have been combined together to deal with a two way duplication mechanisms. A novel framework has been proposed which predicts the label (duplicate or non-duplicate) for any newly arrived bug report. Further, if found as duplicate, the proposed framework produces a ranked list of bug reports which might be similar to the duplicate predicted bug report. The proposed framework has been experimentally validated using bug reports obtained from Eclipse, NetBeans and Mozilla Firefox projects of Bugzilla repository. From the experimental evaluations, we observed that deep learning-based models outperform traditional machine learning algorithms in bug report classification.
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 Intelligent Systems Technologies and Applications (IJISTA):
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