research-article

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Neural program repair with execution-based backpropagation

Authors:

Matias Martinez,

Martin MonperrusAuthors Info & Claims

ICSE '22: Proceedings of the 44th International Conference on Software Engineering

Pages 1506 - 1518

https://doi.org/10.1145/3510003.3510222

Published: 05 July 2022 Publication History

Abstract

Neural machine translation (NMT) architectures have achieved promising results for automatic program repair. Yet, they have the limitation of generating low-quality patches (e.g., not compilable patches). This is because the existing works only optimize a purely syntactic loss function based on characters and tokens without incorporating program-specific information during neural network weight optimization. In this paper, we propose a novel program repair model called RewardRepair. The core novelty of RewardRepair is to improve NMT-based program repair with a loss function based on program compilation and test execution information, rewarding the network to produce patches that compile and that do not overfit. We conduct several experiments to evaluate RewardRepair showing that it is feasible and effective to use compilation and test execution results to optimize the underlying neural repair model. RewardRepair correctly repairs 207 bugs over four benchmarks. we report on repair success for 121 bugs that are fixed for the first time in the literature. Also, RewardRepair produces up to 45.3% of compilable patches, an improvement over the 39% by the state-of-the-art.

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cover image ACM Conferences

ICSE '22: Proceedings of the 44th International Conference on Software Engineering

May 2022

2508 pages

ISBN:9781450392211

DOI:10.1145/3510003

General Chair:
Matthew B Dwyer
University of Virginia
,
Program Chairs:
Daniela Damian
University of Victoria, Canada
,
Andreas Zeller
CISPA, Germany

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May 21 - 29, 2022

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Pei KLi WJin QLiu SGeng SCavallaro LYang JJana SSalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)Exploiting code symmetries for learning program semanticsProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3693695(40092-40113)Online publication date: 21-Jul-2024
https://dl.acm.org/doi/10.5555/3692070.3693695
Hao SShi XLiu H(2024)Exploring the Potential of Pre-Trained Language Models of Code for Automated Program RepairElectronics10.3390/electronics1307120013:7(1200)Online publication date: 25-Mar-2024
https://doi.org/10.3390/electronics13071200
Cao HHan DChu YTian FWang YLiu YJia JGe H(2024)Multi-mechanism neural machine translation framework for automatic program repairJournal of Intelligent & Fuzzy Systems10.3233/JIFS-23403746:4(7859-7873)Online publication date: 18-Apr-2024
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