A Multi-Factor Approach for Selection of Developers to Fix Bugs in a Program

• Main Authors: Shikai Guo, Shifei Chen, Siwen Wang, Decheng Zhang, Yaqing Liu, Chen Guo, Hui Li, Tingting Li
• Format: Article
• Language: English
• Published: MDPI AG 2019-08-01
• Series: Applied Sciences
• Subjects: developer recommendation; composite method; ML-KNN algorithm; relevance score
• Online Access: https://www.mdpi.com/2076-3417/9/16/3327

In a software tracking system, the bug assignment problem refers to the activities that developers perform during software maintenance to fix bugs. As many bugs are submitted on a daily basis, the number of developers required is quite large, and it therefore becomes difficult to assign the right developers to resolve issues with specific bugs. Inappropriate dispatches results in delayed processing of bug reports. In this paper, we propose an algorithm called ABC-DR to solve the bug assignment problem. The ABC-DR algorithm is a two-part composite approach that includes analysis between bug reports (i.e., B-based analysis) and analysis between developers and bug reports (i.e., D-based analysis). For analysis between bug reports, we use the multi-label k-nearest neighbor (ML-KNN) algorithm to find similar bug reports when compared with the new bug reports, and the developers who analyze similar bug reports recommend developers for the new bug report. For analysis between developers and bug reports, we find developer rankings similar to the new bug report by calculating the relevance scores between developers and similar bug reports. We use the artificial bee colony (ABC) algorithm to calculate the weight of each part. We evaluated the proposed algorithms on three datasets—GCC, Mozilla, and NetBeans—comparing ABC-DR with DevRec, DREX, and Bugzie. The experimental results show that the proposed ABC-DR algorithm achieves the highest improvement of 51.2% and 53.56% over DevRec for recall@5 and recall@10 in the NetBeans dataset.