research-article

Program merge conflict resolution via neural transformers

Authors:

Alexey Svyatkovskiy,

Sarah Fakhoury,

Negar Ghorbani,

Todd Mytkowicz,

Elizabeth Dinella,

Christian Bird,

Neel Sundaresan,

Shuvendu K. LahiriAuthors Info & Claims

ESEC/FSE 2022: Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering

Pages 822 - 833

https://doi.org/10.1145/3540250.3549163

Published: 09 November 2022 Publication History

Abstract

Collaborative software development is an integral part of the modern software development life cycle, essential to the success of large-scale software projects. When multiple developers make concurrent changes around the same lines of code, a merge conflict may occur. Such conflicts stall pull requests and continuous integration pipelines for hours to several days, seriously hurting developer productivity. To address this problem, we introduce MergeBERT, a novel neural program merge framework based on token-level three-way differencing and a transformer encoder model. By exploiting the restricted nature of merge conflict resolutions, we reformulate the task of generating the resolution sequence as a classification task over a set of primitive merge patterns extracted from real-world merge commit data. Our model achieves 63–68% accuracy for merge resolution synthesis, yielding nearly a 3× performance improvement over existing semi-structured, and 2× improvement over neural program merge tools. Finally, we demonstrate that MergeBERT is sufficiently flexible to work with source code files in Java, JavaScript, TypeScript, and C# programming languages. To measure the practical use of MergeBERT, we conduct a user study to evaluate MergeBERT suggestions with 25 developers from large OSS projects on 122 real-world conflicts they encountered. Results suggest that in practice, MergeBERT resolutions would be accepted at a higher rate than estimated by automatic metrics for precision and accuracy. Additionally, we use participant feedback to identify future avenues for improvement of MergeBERT.

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Campos Junior HMenezes GBarros MHoek AMurta L(2024)How code composition strategies affect merge conflict resolution?Journal of Software Engineering Research and Development10.5753/jserd.2024.363812:1Online publication date: 31-Oct-2024
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Index Terms

Program merge conflict resolution via neural transformers

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Published In

cover image ACM Conferences

ESEC/FSE 2022: Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering

November 2022

1822 pages

ISBN:9781450394130

DOI:10.1145/3540250

General Chair:
Abhik Roychoudhury
National University of Singapore, Singapore
,
Program Chairs:
Cristian Cadar
Imperial College London, UK
,
Miryung Kim
University of California at Los Angeles, USA

Copyright © 2022 ACM.

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Published: 09 November 2022

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ESEC/FSE '22: 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering

November 14 - 18, 2022

Singapore, Singapore

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https://doi.org/10.1016/j.neunet.2024.107067
Campos Junior HMenezes GBarros MHoek AMurta L(2024)How code composition strategies affect merge conflict resolution?Journal of Software Engineering Research and Development10.5753/jserd.2024.363812:1Online publication date: 31-Oct-2024
https://doi.org/10.5753/jserd.2024.3638
Schesch BFeatherman RYang KRoberts BErnst MFilkov VRay BZhou M(2024)Evaluation of Version Control Merge ToolsProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3695075(831-83)Online publication date: 27-Oct-2024
https://dl.acm.org/doi/10.1145/3691620.3695075
Dong JSun JLin YZhang YMa MDong JHao DFilkov VRay BZhou M(2024)Revisiting the Conflict-Resolving Problem from a Semantic PerspectiveProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3694993(141-152)Online publication date: 27-Oct-2024
https://dl.acm.org/doi/10.1145/3691620.3694993
Boll AVan Dok YOhrndorf MSchultheiß AKehrer T(2024)Towards Semi-Automated Merge Conflict Resolution: Is It Easier Than We Expected?Proceedings of the 28th International Conference on Evaluation and Assessment in Software Engineering10.1145/3661167.3661197(282-292)Online publication date: 18-Jun-2024
https://doi.org/10.1145/3661167.3661197
Wardat MAl-Alaj A(2024)DeepCNN: A Dual Approach to Fault Localization and Repair in Convolutional Neural NetworksIEEE Access10.1109/ACCESS.2024.338498112(50321-50334)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3384981
Li TZhang Y(2024)Multilingual code refactoring detection based on deep learningExpert Systems with Applications10.1016/j.eswa.2024.125164258(125164)Online publication date: Dec-2024
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Muylaert WHärtel JDe Roover C(2023)Symbolic Execution to Detect Semantic Merge Conflicts2023 IEEE 23rd International Working Conference on Source Code Analysis and Manipulation (SCAM)10.1109/SCAM59687.2023.00028(186-197)Online publication date: 2-Oct-2023
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Vale GFernandes EFigueiredo EApel S(2023)Behind Developer Contributions on Conflicting Merge Scenarios2023 IEEE 23rd International Working Conference on Source Code Analysis and Manipulation (SCAM)10.1109/SCAM59687.2023.00014(25-36)Online publication date: 2-Oct-2023
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