Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
5-2015
Abstract
Links between issue reports and their corresponding commits in version control systems are often missing. However, these links are important for measuring the quality of a software system, predicting defects, and many other tasks. Several approaches have been designed to solve this problem by automatically linking bug reports to source code commits via comparison of textual information in commit messages and bug reports. Yet, the effectiveness of these techniques is oftentimes suboptimal when commit messages are empty or contain minimum information; this particular problem makes the process of recovering traceability links between commits and bug reports particularly challenging. In this work, we aim at improving the effectiveness of existing bug linking techniques by utilizing rich contextual information. We rely on a recently proposed approach, namely ChangeScribe, which generates commit messages containing rich contextual information by using code summarization techniques. Our approach then extracts features from these automatically generated commit messages and bug reports, and inputs them into a classification technique that creates a discriminative model used to predict if a link exists between a commit message and a bug report. We compared our approach, coined as RCLinker (Rich Context Linker), to MLink, which is an existing state-of-the-art bug linking approach. Our experiment results on bug reports from six software projects show that RCLinker outperforms MLink in terms of F-measure by 138.66%.
Discipline
Software Engineering
Research Areas
Software and Cyber-Physical Systems
Publication
2015 IEEE 23rd International Conference on Program Comprehension (ICPC 2015): Florence, Italy, May 18-19
First Page
36
Last Page
47
ISBN
9781467381598
Identifier
10.1109/ICPC.2015.13
Publisher
IEEE Computer Society
City or Country
Los Alamitos, CA
Citation
LE, Tien-Duy B.; VASQUEZ, Mario Linares; David LO; and POSHYVANYK, Denys.
RCLinker: Automated Linking of Issue Reports and Commits Leveraging Rich Contextual Information. (2015). 2015 IEEE 23rd International Conference on Program Comprehension (ICPC 2015): Florence, Italy, May 18-19. 36-47.
Available at: https://ink.library.smu.edu.sg/sis_research/3094
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
http://dx.doi.org/10.1109/ICPC.2015.13