Show Me Why It’s Correct: Saving 1/3 of Debugging Time in Program Repair with Interactive Runtime Comparison

Automated Program Repair (APR) holds the promise of alleviating the burden of debugging and fixing software bugs. Despite this, developers still need to manually inspect each patch to confirm its correctness, which is tedious and time-consuming. This challenge is exacerbated in the presence of plausible patches, which accidentally pass test cases but may not correctly fix the bug. To address this challenge, we propose an interactive approach called iFix to facilitate patch understanding and comparison based on their runtime difference. iFix performs static analysis to identify runtime variables related to the buggy statement and captures their runtime values during execution for each patch. These values are then aligned across different patch candidates, allowing users to compare and contrast their runtime behavior. To evaluate iFix, we conducted a within-subjects user study with 28 participants. Compared with manual inspection and a state-of-the-art interactive patch filtering technique, iFix reduced participants’ task completion time by 36% and 33% while also improving their confidence by 50% and 20%, respectively. Besides, quantitative experiments demonstrate that iFix improves the ranking of correct patches by at least 39% compared with other patch ranking methods and is generalizable to different APR tools.

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Publication Details

Journal
Proceedings of the ACM on Programming Languages
Published
2025-04-09
DOI
https://doi.org/10.1145/3720510
Primary Topic
Software Reliability and Analysis Research
Type
article
Field-Weighted Citation Impact
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article

Show Me Why It’s Correct: Saving 1/3 of Debugging Time in Program Repair with Interactive Runtime Comparison

Zhongkai Zhao, Yiling Lou, Ruixin Wang, Tianyi Zhang et al.
Proceedings of the ACM on Programming Languages
Software Reliability and Analysis Research
article

Show Me Why It’s Correct: Saving 1/3 of Debugging Time in Program Repair with Interactive Runtime Comparison

Zhongkai Zhao, Yiling Lou, Ruixin Wang, Tianyi Zhang, Nan Jiang, Le Fang, Lin Tan
article en

Abstract

Automated Program Repair (APR) holds the promise of alleviating the burden of debugging and fixing software bugs. Despite this, developers still need to manually inspect each patch to confirm its correctness, which is tedious and time-consuming. This challenge is exacerbated in the presence of plausible patches, which accidentally pass test cases but may not correctly fix the bug. To address this challenge, we propose an interactive approach called iFix to facilitate patch understanding and comparison based on their runtime difference. iFix performs static analysis to identify runtime variables related to the buggy statement and captures their runtime values during execution for each patch. These values are then aligned across different patch candidates, allowing users to compare and contrast their runtime behavior. To evaluate iFix, we conducted a within-subjects user study with 28 participants. Compared with manual inspection and a state-of-the-art interactive patch filtering technique, iFix reduced participants’ task completion time by 36% and 33% while also improving their confidence by 50% and 20%, respectively. Besides, quantitative experiments demonstrate that iFix improves the ranking of correct patches by at least 39% compared with other patch ranking methods and is generalizable to different APR tools.

Proceedings of the ACM on Programming LanguagesVol. 9(OOPSLA1)
National University of Singapore (SG), Purdue University West Lafayette (US), Fudan University (CN)
Openalex Percentile: Top 94%
Software Reliability and Analysis Research
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