A Comprehensive Study of Automatic Program Repair on the QuixBugs Benchmark

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.

References in corpus (6)

• Nopol: Automatic Repair of Conditional Statement Bugs in Java Programs
• Automatic Software Repair: a Bibliography
• Automatic Repair of Real Bugs in Java: A Large-Scale Experiment on the Defects4J Dataset
• Automated Fixing of Programs with Contracts
• Alleviating Patch Overfitting with Automatic Test Generation: A Study of Feasibility and Effectiveness for the Nopol Repair System
• Astor: Exploring the Design Space of Generate-and-Validate Program Repair beyond GenProg

Cited by in corpus (8)

• Neural Program Repair with Execution-based Backpropagation
• SelfAPR: Self-supervised Program Repair with Test Execution Diagnostics
• Automated Classification of Overfitting Patches with Statically Extracted Code Features
• ITER: Iterative Neural Repair for Multi-Location Patches
• T5APR: Empowering Automated Program Repair across Languages through Checkpoint Ensemble
• A Comprehensive Study of Code-removal Patches in Automated Program Repair
• Assessing the Latent Automated Program Repair Capabilities of Large Language Models using Round-Trip Translation
• MultiMend: Multilingual Program Repair with Context Augmentation and Multi-Hunk Patch Generation