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Publication Additional Information Download
Publication Type
Journal Article
Authorship
Mondal, M., Roy, C. K., Roy, B., Schneider, K. A.
Title
FLeCCS: A Technique for Suggesting Fragment-Level Similar Co-change Candidates
Year
2021
Publication Outlet
2021 IEEE/ACM 29th International Conference on Program Comprehension (ICPC). IEEE.
DOI
https://doi.org/10.1109/icpc52881.2021.00024
Citation
Mondal, M., Roy, C. K., Roy, B., Schneider, K. A. (2021). FLeCCS: A Technique for Suggesting Fragment-Level Similar Co-change Candidates. In 2021 IEEE/ACM 29th International Conference on Program Comprehension (ICPC). IEEE. (). https://doi.org/10.1109/icpc52881.2021.00024
Abstract
When a programmer changes a particular code fragment, the other similar code fragments in the code-base may also need to be changed together (i.e., co-changed) consistently to ensure that the software system remains consistent. Existing studies and tools apply clone detectors to identify these similar co-change candidates for a target code fragment. However, clone detectors suffer from a confounding configuration choice problem and it affects their accuracy in retrieving co-change candidates.In our research, we propose and empirically evaluate a lightweight co-change suggestion technique that can automatically suggest fragment level similar co-change candidates for a target code fragment using WA-DiSC (Weighted Average Dice-Sørensen Co-efficient) through a context-sensitive mining of the entire code-base. We apply our technique, FLeCCS (Fragment Level Co-change Candidate Suggester), on six subject systems written in three different programming languages (Java, C, and C#) and compare its performance with the existing state-of-the-art techniques. According to our experiment, our technique outperforms not only the existing code clone based techniques but also the association rule mining based techniques in detecting co-change candidates with a significantly higher accuracy (precision and recall). We also find that File Proximity Ranking performs significantly better than Similarity Extent Ranking when ranking the co-change candidates suggested by our proposed technique.
Program Affiliations
GWF: Global Water Futures
Project Affiliations
GWF-CS: Computer Science
Publication Stage
Published
Download Links
https://doi.org/10.1109/icpc52881.2021.00024
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