A comprehensive study of automatic program repair on the QuixBugs benchmark

Proceedings of Journal of Systems and Software (JSS)

Y. HeM. MartinezT. DurieuxM. Monperrus 

PDFDOIExperiment Results

Abstract

Automatic program repair papers tend to repeatedly use the same benchmarks. This poses a threat to the external validity of the findings of the program repair research community. In this paper, we perform an empirical study of automatic repair on a benchmark of bugs called QuixBugs, which has been little studied. In this paper, 1) We report on the characteristics of QuixBugs; 2) We study the effectiveness of 10 program repair tools on it; 3) We apply three patch correctness assessment techniques to comprehensively study the presence of overfitting patches in QuixBugs. Our key results are: 1) 16/40 buggy programs in QuixBugs can be repaired with at least a test suite adequate patch; 2) A total of 338 plausible patches are generated on the QuixBugs by the considered tools, and 53.3% of them are overfitting patches according to our manual assessment; 3) The three automated patch correctness assessment techniques, RGT_Evosuite, RGT_InputSampling and GT_Invariants, achieve an accuracy of 98.2%, 80.8% and 58.3% in overfitting detection, respectively. To our knowledge, this is the largest empirical study of automatic repair on QuixBugs, combining both quantitative and qualitative insights. All our empirical results are publicly available on GitHub in order to facilitate future research on automatic program repair.

@article{APR_QuixBugs
author = {He Ye and
Matias Martinez and
Thomas Durieux and
Martin Monperrus},
title = {A comprehensive study of automatic program repair on the QuixBugs
benchmark},
journal = {J. Syst. Softw.},
volume = {171},
pages = {110825},
year = {2021},
url = { https://doi.org/10.1016/j.jss.2020.110825 },
doi = {10.1016/j.jss.2020.110825},
timestamp = {Tue, 29 Dec 2020 18:22:33 +0100},
biburl = { https://dblp.org/rec/journals/jss/YeMDM21.bib },
bibsource = {dblp computer science bibliography,  https://dblp.org }
}
Last Updated: 29/07/2021