arXiv:2605.25890v1 Announce Type: new Abstract: This paper applies machine learning to the difficult and important task of version control merging. (1) We constructed a dataset, Merge-Bench, of 7938 real-world merge conflict hunks from 1439 GitHub repositories. The ground truth is the merge resolution that developers committed to the repository. Our dataset construction methodology is scalable to arbitrary amounts of data since no manual labeling is required. (2) We trained a model, LLMergeJ, to resolve merge conflicts in Java programs. Our approach uses Group Relative Policy Optimization (GRP
The increasing sophistication and capability of large language models are enabling them to tackle previously intractable problems in software development automation, making this development timely.
Automating merge conflict resolution addresses a significant friction point in software development, potentially improving developer productivity and accelerating software delivery cycles.
Traditional manual and often complex merge conflict resolution can now be significantly augmented or even automated by AI, redefining developer workflows and the efficiency of collaborative coding.
- · Software developers
- · Companies with large codebases
- · AI software tool vendors
- · Open-source communities
- · Tasks requiring manual merge conflict resolution
Developers will spend less time on merge conflict resolution, freeing up resources for core development tasks.
Faster and more reliable merging could enable more aggressive continuous integration and continuous delivery (CI/CD) pipelines, accelerating product release cycles.
The success in resolving merge conflicts could lead to broader AI application in other complex software engineering tasks, such as automated refactoring or bug fixing, further automating the software development lifecycle.
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Read at arXiv cs.LG