The Impact of Program Reduction on Automated Program Repair

Correcting bugs using modern Automated Program Repair (APR) can be both time-consuming and resource-expensive. We describe a program repair approach that aims to improve the scalability of modern APR tools. The approach leverages program reduction in the form of program slicing to eliminate code irrelevant to fixing the bug, which improves the APR tool's overall performance. We investigate slicing's impact on all three phases of the repair process: fault localization, patch generation, and patch validation. Our empirical exploration finds that the proposed approach on average enhances the repair ability of the TBar APR tool, but we also discovered a few cases where it was less successful. Specifically, on examples from the widely used Defects4J dataset, we obtain a substantial reduction in median repair time, which falls from 80 minutes to just under 18 minutes. We conclude that program reduction can improve the performance of APR without degrading repair quality, but this improvement is not universal.

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Publication Details

Published
2024-10-06
DOI
https://doi.org/10.1109/icsme58944.2024.00039
Citations
1
Primary Topic
Parallel Computing and Optimization Techniques
Type
article
Field-Weighted Citation Impact
0.49

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article

The Impact of Program Reduction on Automated Program Repair

David Binkley, Leon Moonen, Linas Vidziunas
1 citations
Parallel Computing and Optimization Techniques
0.49
article

The Impact of Program Reduction on Automated Program Repair

David Binkley, Leon Moonen, Linas Vidziunas
article en
1 citations

Abstract

Correcting bugs using modern Automated Program Repair (APR) can be both time-consuming and resource-expensive. We describe a program repair approach that aims to improve the scalability of modern APR tools. The approach leverages program reduction in the form of program slicing to eliminate code irrelevant to fixing the bug, which improves the APR tool's overall performance. We investigate slicing's impact on all three phases of the repair process: fault localization, patch generation, and patch validation. Our empirical exploration finds that the proposed approach on average enhances the repair ability of the TBar APR tool, but we also discovered a few cases where it was less successful. Specifically, on examples from the widely used Defects4J dataset, we obtain a substantial reduction in median repair time, which falls from 80 minutes to just under 18 minutes. We conclude that program reduction can improve the performance of APR without degrading repair quality, but this improvement is not universal.

Loyola University Maryland (US), BI Norwegian Business School (NO), Simula Research Laboratory (NO)
Norges Forskningsråd
Openalex Percentile: Top 33%
Parallel Computing and Optimization Techniques
0.49
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