Refactoring Inspection Support for Manual Refactoring Edits (TSE 2017) https://sites.google.com/site/refdistiller/
Manual refactoring edits are error prone, as refactoring requires developers to coordinate related transformations and understand the complex inter-relationship between affected files, variables, and methods. We propose RefDistiller, an approach for improving detection of manual refactoring anomalies by two combined strategies. First, it uses a predefined template to identify potential missed refactoring edits---omission anomalies. Second, it leverages an automated refactoring engine to separate behavior-preserving edits from behavior-modifying edits---commission anomalies. We evaluate its effectiveness on a data set with one hundred manual refactoring bugs. These bugs are hard to detect because they do not produce any compilation errors nor are caught by the pre- and post-condition checking of many existing refactoring engines. RefDistiller is able to identify 97% of the erroneous edits, of which 24% are not detected by the given test suites.
This project is developed by Professor Miryung Kim's group. If you encounter any problems, please open an issue or feel free to contact us:
Everton: Researcher at Federal University of Campina Grande, everton@computacao.ufcg.edu.br;
Myoungkyu: Assistant Professor at UNO, myoungkyu@unomaha.edu;
Please refer to our TSE'17 paper, Refactoring Inspection Support for Manual Refactoring Edits for more details.
@ARTICLE{RefDistiller, author={Alves, Everton L. G. and Song, Myoungkyu and Massoni, Tiago and Machado, Patrícia D. L. and Kim, Miryung}, journal={IEEE Transactions on Software Engineering}, title={Refactoring Inspection Support for Manual Refactoring Edits}, year={2018}, volume={44}, number={4}, pages={365-383}, doi={10.1109/TSE.2017.2679742}}
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