Title: A novel framework for labelling duplicate and non-duplicate bugs

Authors: Kulbhushan Bansal; Sunesh Malik; Manju Rohil; Harish Rohil

Addresses: Department of CSE, Chaudhary Devi Lal University, Sirsa, Haryana, India ' Department of Information Technology, Maharaja Surajmal Institute of Technology, New Delhi, India ' CDL Govertment Polytechnic, Nathusari Chopta, Sirsa, Haryana, India ' Department of CSE, Chaudhary Devi Lal University, Sirsa, Haryana, India

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.

Keywords: duplicate bug detection; information retrieval; machine learning; classification; deep learning; rank aggregation; mining software repositories.

DOI: 10.1504/IJISTA.2023.131567

International Journal of Intelligent Systems Technologies and Applications, 2023 Vol.21 No.2, pp.93 - 128

Received: 09 Dec 2022
Accepted: 27 Feb 2023
Published online: 19 Jun 2023 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article