0000000000425182

AUTHOR

Martin Monperrus

0000-0003-3505-3383

showing 2 related works from this author

A comprehensive study of automatic program repair on the QuixBugs benchmark

2021

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 pa…

FOS: Computer and information sciencesCorrectnessComputer science02 engineering and technologyOverfittingMachine learningcomputer.software_genreMaintenance engineeringExternal validityComputer Science - Software Engineering020204 information systems0202 electrical engineering electronic engineering information engineeringTest suite[INFO]Computer Science [cs]computer.programming_languagebusiness.industry020207 software engineeringSoftware maintenancePython (programming language)Software Engineering (cs.SE)Software bugHardware and ArchitectureBenchmark (computing)Artificial intelligencebusinesscomputerSoftwareInformation Systems
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Automated Patch Assessment for Program Repair at Scale

2021

AbstractIn 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 h…

FOS: Computer and information sciencesGround truthCorrectnessComputer sciencebusiness.industryRandom testing020207 software engineering02 engineering and technologyOverfittingMachine learningcomputer.software_genreOracleSoftware Engineering (cs.SE)External validityComputer Science - Software Engineering020204 information systems0202 electrical engineering electronic engineering information engineering[INFO]Computer Science [cs]State (computer science)Artificial intelligencebusinessScale (map)computerSoftware
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