Work

Automated patch assessment for program repair at scale

Year: 2021

Type: article

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.

Source: Empirical Software Engineering

DOI: 10.1007/s10664-020-09920-w

Authors: He Ye, Matias Martinez, Martin Monperrus

Institutions: KTH Royal Institute of Technology, Université de Valenciennes et du Hainaut-Cambrésis, Université Polytechnique Hauts-de-France

Language: English

Topic: Software Testing and Debugging Techniques

Subfield: Software

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $2,780

Funder: Kungliga Tekniska Högskolan