# What a diff makes: automating code migration with large language models

arXiv: https://arxiv.org/abs/2511.00160 · HTML: https://mlsanity.com/paper/2511.00160/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 31 references that are in the map.
- First posted on arXiv 31 Oct 2025.

## Builds on (most cited first, 16 of 16)

- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [SWE-bench: Can Language Models Resolve Real-World GitHub Issues?](https://mlsanity.com/paper/2310.06770/) (2023)
- [SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering](https://mlsanity.com/paper/2405.15793/) (2024)
- [Gorilla: Large Language Model Connected with Massive APIs](https://mlsanity.com/paper/2305.15334/) (2023)
- [Long-context LLMs Struggle with Long In-context Learning](https://mlsanity.com/paper/2404.02060/) (2024)
- [Same Task, More Tokens: the Impact of Input Length on the Reasoning Performance of Large Language Models](https://mlsanity.com/paper/2402.14848/) (2024)
- [Leveraging Automated Unit Tests for Unsupervised Code Translation](https://mlsanity.com/paper/2110.06773/) (2021)
- [Code Repair with LLMs gives an Exploration-Exploitation Tradeoff](https://mlsanity.com/paper/2405.17503/) (2024)
- [CoditT5: Pretraining for Source Code and Natural Language Editing](https://mlsanity.com/paper/2208.05446/) (2022)
- [Teaching Code LLMs to Use Autocompletion Tools in Repository-Level Code Generation](https://mlsanity.com/paper/2401.06391/) (2024)
- [CodeRosetta: Pushing the Boundaries of Unsupervised Code Translation for Parallel Programming](https://mlsanity.com/paper/2410.20527/) (2024)
- [Exploring the Capabilities of LLMs for Code Change Related Tasks](https://mlsanity.com/paper/2407.02824/) (2024)
- [StackEval: Benchmarking LLMs in Coding Assistance](https://mlsanity.com/paper/2412.05288/) (2024)
- [How Different Are Different diff Algorithms in Git?](https://mlsanity.com/paper/1902.02467/) (2019)
- [DiffSpec: Differential Testing with LLMs using Natural Language Specifications and Code Artifacts](https://mlsanity.com/paper/2410.04249/) (2024)
- [Parcels v0.9: prototyping a Lagrangian Ocean Analysis framework for the petascale age](https://mlsanity.com/paper/1707.05163/) (2017)

## Abstract (arXiv)

Modern software programs are built on stacks that are often undergoing changes that introduce updates and improvements, but may also break any project that depends upon them. In this paper we explore the use of Large Language Models (LLMs) for code migration, specifically the problem of maintaining compatibility with a dependency as it undergoes major and minor semantic version changes. We demonstrate, using metrics such as test coverage and change comparisons, that contexts containing diffs can significantly improve performance against out of the box LLMs and, in some cases, perform better than using code. We provide a dataset to assist in further development of this problem area, as well as an open-source Python package, AIMigrate, that can be used to assist with migrating code bases. In a real-world migration of TYPHOIDSIM between STARSIM versions, AIMigrate correctly identified 65% of required changes in a single run, increasing to 80% with multiple runs, with 47% of changes generated perfectly.