Automated patch assessment for program repair at scale

He Ye (1) , Matias Martinez (2) , Martin Monperrus (1)

1 KTH - KTH Royal Institute of Technology [Stockholm]
2 LAMIH - Laboratoire d'Automatique, de Mécanique et d'Informatique industrielles et Humaines - UMR 8201

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.

Mots clés: Automatic program repair; Automatic patch assessment

Domaines: Génie logiciel [cs.SE]

He Ye, Matias Martinez, Martin Monperrus. Automated patch assessment for program repair at scale. Empirical Software Engineering, 2021, 26 (2), pp.20. ⟨10.1007/s10664-020-09920-w⟩. ⟨hal-03396298⟩