Paper Info
Title
Learning To Rank Developers For Bug Report Assignment
Abstract
Bug assignment is a burden for projects receiving many bug reports. To automate the process of assigning bug reports to the appropriate developers, several studies have relied on combining natural language processing and information retrieval techniques to extract two categories of features. One of these categories targets developers who have fixed similar bugs before, and the other determines developers working on source files similar to the description of the bug. Commit messages represent another rich source for profiling developer expertise as the language used in commit messages is closer to that used in bug reports.In this work, we propose a more enhanced profiling of developers through their commits, which are captured in a new set of features that we combine with features used in previous studies. More precisely, we propose an adaptive ranking approach that takes as input a given bug report and ranks the top developers who are most suitable to fix it. This approach learns from the history of previously fixed bugs to profile developers in terms of their expertise. With respect to a given bug report, the ranking score of each developer is computed as a weighted combination of an array of features encoding domain knowledge, where the weights are trained automatically on previously solved bug reports using a learning-to-rank technique. Our model was evaluated using around 22,000 bug reports, exported from four large scale open-source Java projects. Results show that our model significantly outperformed two recent state-of-the-art methods in recommending the suitable developer to handle a certain bug report. Specifically, the percentage of recommending a developer within the top 5 ranked developers correctly was over 80% for both the Eclipse UI Platform and Birt projects. (C) 2020 Elsevier B.V. All rights reserved.
Year
DOI
Venue
2020
10.1016/j.asoc.2020.106667
APPLIED SOFT COMPUTING
Keywords
DocType
Volume
Bug report, Bug assignment, Learning to rank, Software quality, Mining software repositories
Journal
95
ISSN
Citations 
PageRank 
1568-4946
4
0.39
References 
Authors
42
6
Name
Order
Citations
PageRank
Bader Alkhazi140.39
Andrew DiStasi240.39
Wajdi Aljedaani3122.21
Hussein Alrubaye471.54
Xin Ye51776.08
Mohamed Wiem Mkaouer622828.58