Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
11-2021
Abstract
Numerous efforts have been invested in improving the effectiveness of bug localization techniques, whereas little attention is paid to making these tools run more efficiently in continuously evolving software repositories. This paper first analyzes the information retrieval model behind a classic bug localization tool, BugLocator, and builds a mathematical foundation illustrating that the model can be updated incrementally when codebase or bug reports evolve. Then, we present IncBL, a tool for Incremental Bug Localization in evolving software repositories. IncBL is evaluated on the Bugzbook dataset, and the results show that IncBL can significantly reduce the running time by 77.79% on average compared with the re-computing the model, while maintaining the same level of accuracy. We also implement IncBL as a Github App that can be easily integrated into open-source projects on GitHub. Users can deploy and use IncBL locally as well. The demo video for IncBL can be viewed at https://youtu.be/G4gMuvlJSb0, and the source code can be found at https://github.com/soarsmu/IncBL.
Discipline
Databases and Information Systems
Research Areas
Data Science and Engineering
Publication
Proceedings of the 36th IEEE/ACM International Conference on Automated Software Engineering (ASE 2021), Virtual, November 14-20
City or Country
Melbourne, Australia
Citation
YANG, Zhou; SHI, Jieke; WANG Shaowei; and LO, David.
IncBL: Incremental Bug Localization. (2021). Proceedings of the 36th IEEE/ACM International Conference on Automated Software Engineering (ASE 2021), Virtual, November 14-20.
Available at: https://ink.library.smu.edu.sg/sis_research/6895
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.