The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Learning to rank relevant files for bug reports using domain knowledge
Abstract
When a new bug report is received, developers usually need to reproduce the bug and perform code reviews to find the cause, a process that can be tedious and time consuming. A tool for ranking all the source files of a project with respect to how likely they are to contain the cause of the bug would enable developers to narrow down their search and potentially could lead to a substantial increase in productivity. This paper introduces an adaptive ranking approach that leverages domain knowledge through functional decompositions of source code files into methods, API descriptions of library components used in the code, the bug-fixing history, and the code change history. Given a bug report, the ranking score of each source file 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. We evaluated our system on six large scale open source Java projects, using the before-fix version of the project for every bug report. The experimental results show that the newly introduced learning-to-rank approach significantly outperforms two recent state-of-the-art methods in recommending relevant files for bug reports. In particular, our method makes correct recommendations within the top 10 ranked source files for over 70% of the bug reports in the Eclipse Platform and Tomcat projects.
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Cites
Introduction to information retrieval
(2009) · Choice Reviews Online · doi:10.5860/choice.46-2715 · Cited by 12,278
Hard-to-answer questions about code
LaToza, T. D., & Myers, B. A. (2010) · Evaluation and Usability of Programming Languages and Tools · doi:10.1145/1937117.1937125 · Cited by 141
Cited by
A survey on deep learning for software engineering
Yang, Y., Xia, X., Lo, D., & Grundy, J. (2022) · ACM Computing Surveys · doi:10.1145/3505243 · Cited by 223
CORE: Automating Review Recommendation for Code Changes
Siow, J. K., Gao, C., Fan, L., Chen, S., & Liu, Y. (2020) · 2020 IEEE 27th International Conference on Software Analysis, Evolution and Reengineering (SANER) · doi:10.1109/saner48275.2020.9054794 · Cited by 56
Ma, Y.-F., Du, Y., & Li, M. (2023) · Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2023/249 · Cited by 4
Metadata
- Authors
- Ye, X., Bunescu, R., & Liu, C.
- Venue
- Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering
- Publisher
- ACM
- Published
- 2014-11-11
- Pages
- 689-699
- OpenAlex
- W2058230372
- Cited by
- 290
- References
- 2
- FWCI
- 15.67
- Type
- conference paper
- Language
- en
- Open access
- closed
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