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arXiv · 1909.13694

Automated Patch Assessment for Program Repair at Scale

He Ye·Matias Martinez·Martin Monperrus
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Abstract

In this paper, we do automatic correctness assessment for patches generated by program repair systems. We consider the human-written patch as ground truth oracle and randomly generate tests based on it, a technique proposed by Shamshiri et al., called Random testing with Ground Truth (RGT) in this paper. We build a curated dataset of 638 patches for Defects4J generated by 14 state-of-the-art repair systems, we evaluate automated patch assessment on this dataset. The results of this study are novel and significant: First, we improve the state of the art performance of automatic patch assessment with RGT by 190% by improving the oracle; Second, we show that RGT is reliable enough to help scientists to do overfitting analysis when they evaluate program repair systems; Third, we improve the external validity of the program repair knowledge with the largest study ever.

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He Ye, Matias Martinez, Martin Monperrus. 2019-09-30. Automated Patch Assessment for Program Repair at Scale. https://doi.org/10.1007/s10664-020-09920-w

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