# Program Merge Conflict Resolution via Neural Transformers

arXiv: https://arxiv.org/abs/2109.00084 · HTML: https://mlsanity.com/paper/2109.00084/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 50 references that are in the map.
- First posted on arXiv 31 Aug 2021.

## Builds on (most cited first, 11 of 11)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://mlsanity.com/paper/2002.08155/) (2020)
- [BLEURT: Learning Robust Metrics for Text Generation](https://mlsanity.com/paper/2004.04696/) (2020)
- [IntelliCode Compose: Code Generation Using Transformer](https://mlsanity.com/paper/2005.08025/) (2020)
- [A Survey of Machine Learning for Big Code and Naturalness](https://mlsanity.com/paper/1709.06182/) (2017)
- [PyMT5: multi-mode translation of natural language and Python code with transformers](https://mlsanity.com/paper/2010.03150/) (2020)
- [Big Code != Big Vocabulary: Open-Vocabulary Models for Source Code](https://mlsanity.com/paper/2003.07914/) (2020)
- [Can Program Synthesis be Used to Learn Merge Conflict Resolutions? An Empirical Analysis](https://mlsanity.com/paper/2103.02004/) (2021)
- [DeepMerge: Learning to Merge Programs](https://mlsanity.com/paper/2105.07569/) (2021)
- [Automatic Detection and Resolution of Software Merge Conflicts: Are We There Yet?](https://mlsanity.com/paper/2102.11307/) (2021)

## Built on by (most cited first, 5 of 5)

- [ChatGPT: A Study on its Utility for Ubiquitous Software Engineering Tasks](https://mlsanity.com/paper/2305.16837/) (2023)
- [GitGoodBench: A Novel Benchmark For Evaluating Agentic Performance On Git](https://mlsanity.com/paper/2505.22583/) (2025)
- [AgenticFlict: A Large-Scale Dataset of Merge Conflicts in AI Coding Agent Pull Requests on GitHub](https://mlsanity.com/paper/2604.03551/) (2026)
- [Rover: Context-aware Conflict Resolution with LLM](https://mlsanity.com/paper/2605.17279/) (2026)
- [Merge-Bench: Resolve Merge Conflicts with Large Language Models](https://mlsanity.com/paper/2605.25890/) (2026)

## Abstract (arXiv)

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 3x performance improvement over existing semi-structured, and 2x 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.