TL;DR
This paper presents MergeBERT, a neural transformer-based framework that automates merge conflict resolution in software development, significantly improving accuracy and efficiency across multiple programming languages and validated by a user study.
Contribution
Introduction of MergeBERT, a novel neural program merge framework utilizing token-level differencing and transformer models for conflict resolution.
Findings
Achieves 63-68% accuracy in merge resolution synthesis.
Nearly 3x performance improvement over existing tools.
Validated with a user study involving 25 developers across four programming languages.
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…
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