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Patch correctness assessment in automated program repair based on the impact of patches on production and test code

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Andrian MarcusAuthors Info & Claims

ISSTA 2022: Proceedings of the 31st ACM SIGSOFT International Symposium on Software Testing and Analysis

Pages 654 - 665

https://doi.org/10.1145/3533767.3534368

Published: 18 July 2022 Publication History

Abstract

Test-based generate-and-validate automated program repair (APR) systems often generate many patches that pass the test suite without fixing the bug. The generated patches must be manually inspected by the developers, so previous research proposed various techniques for automatic correctness assessment of APR-generated patches. Among them, dynamic patch correctness assessment techniques rely on the assumption that, when running the originally passing test cases, the correct patches will not alter the program behavior in a significant way, e.g., removing the code implementing correct functionality of the program. In this paper, we propose and evaluate a novel technique, named Shibboleth, for automatic correctness assessment of the patches generated by test-based generate-and-validate APR systems. Unlike existing works, the impact of the patches is captured along three complementary facets, allowing more effective patch correctness assessment. Specifically, we measure the impact of patches on both production code (via syntactic and semantic similarity) and test code (via code coverage of passing tests) to separate the patches that result in similar programs and that do not delete desired program elements. Shibboleth assesses the correctness of patches via both ranking and classification. We evaluated Shibboleth on 1,871 patches, generated by 29 Java-based APR systems for Defects4J programs. The technique outperforms state-of-the-art ranking and classification techniques. Specifically, in our ranking data set, in 43% (66%) of the cases, Shibboleth ranks the correct patch in top-1 (top-2) positions, and in classification mode applied on our classification data set, it achieves an accuracy and F1-score of 0.887 and 0.852, respectively.

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Cited By

Kim YPark YHan SYi JFilkov VRay BZhou M(2024)Enhancing the Efficiency of Automated Program Repair via Greybox AnalysisProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3695602(1719-1731)Online publication date: 27-Oct-2024
https://dl.acm.org/doi/10.1145/3691620.3695602
Petke JMartinez MKechagia MAleti ASarro Fd'Amorim M(2024)The Patch Overfitting Problem in Automated Program Repair: Practical Magnitude and a Baseline for Realistic BenchmarkingCompanion Proceedings of the 32nd ACM International Conference on the Foundations of Software Engineering10.1145/3663529.3663776(452-456)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3663529.3663776
Molina FCopia JGorla AChristakis MPradel M(2024)FixCheck: A Tool for Improving Patch Correctness AnalysisProceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis10.1145/3650212.3685308(1856-1860)Online publication date: 11-Sep-2024
https://dl.acm.org/doi/10.1145/3650212.3685308
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Index Terms

Patch correctness assessment in automated program repair based on the impact of patches on production and test code
1. Software and its engineering
  1. Software creation and management
    1. Software verification and validation
      1. Software defect analysis
        Software testing and debugging

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cover image ACM Conferences

ISSTA 2022: Proceedings of the 31st ACM SIGSOFT International Symposium on Software Testing and Analysis

July 2022

808 pages

ISBN:9781450393799

DOI:10.1145/3533767

General Chair:
Sukyoung Ryu
KAIST, South Korea
,
Program Chair:
Yannis Smaragdakis
University of Athens, Greece

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Published: 18 July 2022

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ISSTA '22: 31st ACM SIGSOFT International Symposium on Software Testing and Analysis

July 18 - 22, 2022

Virtual, South Korea

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Kim YPark YHan SYi JFilkov VRay BZhou M(2024)Enhancing the Efficiency of Automated Program Repair via Greybox AnalysisProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3695602(1719-1731)Online publication date: 27-Oct-2024
https://dl.acm.org/doi/10.1145/3691620.3695602
Petke JMartinez MKechagia MAleti ASarro Fd'Amorim M(2024)The Patch Overfitting Problem in Automated Program Repair: Practical Magnitude and a Baseline for Realistic BenchmarkingCompanion Proceedings of the 32nd ACM International Conference on the Foundations of Software Engineering10.1145/3663529.3663776(452-456)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3663529.3663776
Molina FCopia JGorla AChristakis MPradel M(2024)FixCheck: A Tool for Improving Patch Correctness AnalysisProceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis10.1145/3650212.3685308(1856-1860)Online publication date: 11-Sep-2024
https://dl.acm.org/doi/10.1145/3650212.3685308
Chen XXu FHuang YZhang NZheng Z(2024)JIT-Smart: A Multi-task Learning Framework for Just-in-Time Defect Prediction and LocalizationProceedings of the ACM on Software Engineering10.1145/36437271:FSE(1-23)Online publication date: 12-Jul-2024
https://dl.acm.org/doi/10.1145/3643727
Zhou XXu BKim KHan DNguyen HLe-Cong THe JLe BLo D(2024)Leveraging Large Language Model for Automatic Patch Correctness AssessmentIEEE Transactions on Software Engineering10.1109/TSE.2024.345225250:11(2865-2883)Online publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1109/TSE.2024.3452252
Xiao YYang CWang BXiong Y(2024)Accelerating Patch Validation for Program Repair With Interception-Based Execution SchedulingIEEE Transactions on Software Engineering10.1109/TSE.2024.335996950:3(618-635)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1109/TSE.2024.3359969
Molina FCopia JGorla A(2024)Improving Patch Correctness Analysis via Random Testing and Large Language Models2024 IEEE Conference on Software Testing, Verification and Validation (ICST)10.1109/ICST60714.2024.00036(317-328)Online publication date: 27-May-2024
https://doi.org/10.1109/ICST60714.2024.00036
Zhang QFang CMa YSun WChen Z(2023)A Survey of Learning-based Automated Program RepairACM Transactions on Software Engineering and Methodology10.1145/363197433:2(1-69)Online publication date: 23-Dec-2023
https://dl.acm.org/doi/10.1145/3631974
Kim YHan SKhamit AYi JJust RFraser G(2023)Automated Program Repair from Fuzzing PerspectiveProceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis10.1145/3597926.3598101(854-866)Online publication date: 12-Jul-2023
https://dl.acm.org/doi/10.1145/3597926.3598101
Anjum MSinghal SKapur PKhatri SPanwar S(2023)Analysis of vulnerability fixing process in the presence of incorrect patchesJournal of Systems and Software10.1016/j.jss.2022.111525195:COnline publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1016/j.jss.2022.111525
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