{"title":"Test-based patch clustering for automatically-generated patches assessment","authors":"Matias Martinez, Maria Kechagia, Anjana Perera, Justyna Petke, Federica Sarro, Aldeida Aleti","doi":"10.1007/s10664-024-10503-2","DOIUrl":null,"url":null,"abstract":"<p>Previous studies have shown that Automated Program Repair (<span>apr</span>) techniques suffer from the overfitting problem. Overfitting happens when a patch is run and the test suite does not reveal any error, but the patch actually does not fix the underlying bug or it introduces a new defect that is not covered by the test suite. Therefore, the patches generated by <span>apr</span> tools need to be validated by human programmers, which can be very costly, and prevents <span>apr</span> tool adoption in practice. Our work aims to minimize the number of plausible patches that programmers have to review, thereby reducing the time required to find a correct patch. We introduce a novel light-weight test-based patch clustering approach called <span>xTestCluster</span>, which clusters patches based on their dynamic behavior. <span>xTestCluster</span> is applied after the patch generation phase in order to analyze the generated patches from one or more repair tools and to provide more information about those patches for facilitating patch assessment. The novelty of <span>xTestCluster</span> lies in using information from execution of newly generated test cases to cluster patches generated by multiple APR approaches. A cluster is formed of patches that fail on the same generated test cases. The output from <span>xTestCluster</span> gives developers <i>a)</i> a way of reducing the number of patches to analyze, as they can focus on analyzing a sample of patches from each cluster, <i>b)</i> additional information (new test cases and their results) attached to each patch. After analyzing 902 plausible patches from 21 Java <span>apr</span> tools, our results show that <span>xTestCluster</span> is able to reduce the number of patches to review and analyze with a median of 50%. <span>xTestCluster</span> can save a significant amount of time for developers that have to review the multitude of patches generated by <span>apr</span> tools, and provides them with new test cases that expose the differences in behavior between generated patches. Moreover, <span>xTestCluster</span> can complement other patch assessment techniques that help detect patch misclassifications.</p>","PeriodicalId":11525,"journal":{"name":"Empirical Software Engineering","volume":"35 1","pages":""},"PeriodicalIF":3.5000,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Empirical Software Engineering","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s10664-024-10503-2","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, SOFTWARE ENGINEERING","Score":null,"Total":0}
引用次数: 0
Abstract
Previous studies have shown that Automated Program Repair (apr) techniques suffer from the overfitting problem. Overfitting happens when a patch is run and the test suite does not reveal any error, but the patch actually does not fix the underlying bug or it introduces a new defect that is not covered by the test suite. Therefore, the patches generated by apr tools need to be validated by human programmers, which can be very costly, and prevents apr tool adoption in practice. Our work aims to minimize the number of plausible patches that programmers have to review, thereby reducing the time required to find a correct patch. We introduce a novel light-weight test-based patch clustering approach called xTestCluster, which clusters patches based on their dynamic behavior. xTestCluster is applied after the patch generation phase in order to analyze the generated patches from one or more repair tools and to provide more information about those patches for facilitating patch assessment. The novelty of xTestCluster lies in using information from execution of newly generated test cases to cluster patches generated by multiple APR approaches. A cluster is formed of patches that fail on the same generated test cases. The output from xTestCluster gives developers a) a way of reducing the number of patches to analyze, as they can focus on analyzing a sample of patches from each cluster, b) additional information (new test cases and their results) attached to each patch. After analyzing 902 plausible patches from 21 Java apr tools, our results show that xTestCluster is able to reduce the number of patches to review and analyze with a median of 50%. xTestCluster can save a significant amount of time for developers that have to review the multitude of patches generated by apr tools, and provides them with new test cases that expose the differences in behavior between generated patches. Moreover, xTestCluster can complement other patch assessment techniques that help detect patch misclassifications.
期刊介绍：
Empirical Software Engineering provides a forum for applied software engineering research with a strong empirical component, and a venue for publishing empirical results relevant to both researchers and practitioners. Empirical studies presented here usually involve the collection and analysis of data and experience that can be used to characterize, evaluate and reveal relationships between software development deliverables, practices, and technologies. Over time, it is expected that such empirical results will form a body of knowledge leading to widely accepted and well-formed theories.
The journal also offers industrial experience reports detailing the application of software technologies - processes, methods, or tools - and their effectiveness in industrial settings.
Empirical Software Engineering promotes the publication of industry-relevant research, to address the significant gap between research and practice.
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