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ITER: Iterative Neural Repair for Multi-Location Patches

Authors:

Martin MonperrusAuthors Info & Claims

ICSE '24: Proceedings of the 46th IEEE/ACM International Conference on Software Engineering

Article No.: 10, Pages 1 - 13

https://doi.org/10.1145/3597503.3623337

Published: 06 February 2024 Publication History

Abstract

Automated program repair (APR) has achieved promising results, especially using neural networks. Yet, the overwhelming majority of patches produced by APR tools are confined to one single location. When looking at the patches produced with neural repair, most of them fail to compile, while a few uncompilable ones go in the right direction. In both cases, the fundamental problem is to ignore the potential of partial patches. In this paper, we propose an iterative program repair paradigm called ITER founded on the concept of improving partial patches until they become plausible and correct. First, ITER iteratively improves partial single-location patches by fixing compilation errors and further refining the previously generated code. Second, ITER iteratively improves partial patches to construct multi-location patches, with fault localization re-execution. ITER is implemented for Java based on battle-proven deep neural networks and code representation. ITER is evaluated on 476 bugs from 10 open-source projects in Defects4J 2.0. ITER succeeds in repairing 15.5% of them, including 9 uniquely repaired multi-location bugs.

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Cited By

Huang KXu ZYang SSun HLi XYan ZZhang Y(2024)Evolving Paradigms in Automated Program Repair: Taxonomy, Challenges, and OpportunitiesACM Computing Surveys10.1145/369645057:2(1-43)Online publication date: 10-Oct-2024
https://dl.acm.org/doi/10.1145/3696450
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
Xin QWu HTang JLiu XReiss SXuan J(2024)Detecting, Creating, Repairing, and Understanding Indivisible Multi-Hunk BugsProceedings of the ACM on Software Engineering10.1145/36608281:FSE(2747-2770)Online publication date: 12-Jul-2024
https://dl.acm.org/doi/10.1145/3660828
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cover image ACM Conferences

ICSE '24: Proceedings of the IEEE/ACM 46th International Conference on Software Engineering

May 2024

2942 pages

ISBN:9798400702174

DOI:10.1145/3597503

Co-chairs:
Ana Paiva,
Rui Abreu,
Program Co-chairs:
Abhik Roychoudhury,
Margaret Storey

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Published: 06 February 2024

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ICSE '24: IEEE/ACM 46th International Conference on Software Engineering

April 14 - 20, 2024

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Overall Acceptance Rate 276 of 1,856 submissions, 15%

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Cited By

Huang KXu ZYang SSun HLi XYan ZZhang Y(2024)Evolving Paradigms in Automated Program Repair: Taxonomy, Challenges, and OpportunitiesACM Computing Surveys10.1145/369645057:2(1-43)Online publication date: 10-Oct-2024
https://dl.acm.org/doi/10.1145/3696450
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
Xin QWu HTang JLiu XReiss SXuan J(2024)Detecting, Creating, Repairing, and Understanding Indivisible Multi-Hunk BugsProceedings of the ACM on Software Engineering10.1145/36608281:FSE(2747-2770)Online publication date: 12-Jul-2024
https://dl.acm.org/doi/10.1145/3660828
Chen YJabbarvand RChristakis MPradel M(2024)Neurosymbolic Repair of Test FlakinessProceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis10.1145/3650212.3680369(1402-1414)Online publication date: 11-Sep-2024
https://dl.acm.org/doi/10.1145/3650212.3680369
Xu KZhang GYin XZhuo CSchlichtmann ULi B(2024)Automated C/C++ Program Repair for High-Level Synthesis via Large Language Models2024 ACM/IEEE 6th Symposium on Machine Learning for CAD (MLCAD)10.1109/MLCAD62225.2024.10740262(1-9)Online publication date: 9-Sep-2024
https://doi.org/10.1109/MLCAD62225.2024.10740262
Zhang YWang Y(2024)SeTemAPR: Incorporating Semantic Knowledge in Template-based Neural Program Repair2024 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN60899.2024.10650123(1-8)Online publication date: 30-Jun-2024
https://doi.org/10.1109/IJCNN60899.2024.10650123
Vidziunas LBinkley DMoonen L(2024)The Impact of Program Reduction on Automated Program Repair2024 IEEE International Conference on Software Maintenance and Evolution (ICSME)10.1109/ICSME58944.2024.00039(337-349)Online publication date: 6-Oct-2024
https://doi.org/10.1109/ICSME58944.2024.00039
Gharibi RSadreddini MFakhrahmad S(2024)T5APRJournal of Systems and Software10.1016/j.jss.2024.112083214:COnline publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1016/j.jss.2024.112083

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