A Comprehensive Study of Automatic Program Repair on the QuixBugs Benchmark
This addresses the external validity threat for the program repair research community by providing a comprehensive analysis on an understudied benchmark, though it is incremental as it applies existing methods to new data.
The paper tackles the problem of limited benchmark diversity in automatic program repair by empirically studying the QuixBugs benchmark, finding that 16/40 buggy programs can be repaired and 53.3% of plausible patches are overfitting, with automated detection techniques achieving up to 98.2% accuracy.
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.