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

arXiv 2511.00160 ↗First posted 31 Oct 2025cs.SE cs.AIcited by 0

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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.

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.

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References · 16

  1. 2021Evaluating Large Language Models Trained on Codecited by 8,320
  2. 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,373
  3. 2024SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineeringcited by 1,737
  4. 2023Gorilla: Large Language Model Connected with Massive APIscited by 1,332
  5. 2024Long-context LLMs Struggle with Long In-context Learningcited by 286
  6. 2024Same Task, More Tokens: the Impact of Input Length on the Reasoning Performance of Large Language Modelscited by 152
  7. 2021Leveraging Automated Unit Tests for Unsupervised Code Translationcited by 82
  8. 2024Code Repair with LLMs gives an Exploration-Exploitation Tradeoffcited by 51
  9. 2022CoditT5: Pretraining for Source Code and Natural Language Editingcited by 45
  10. 2024Teaching Code LLMs to Use Autocompletion Tools in Repository-Level Code Generationcited by 30
  11. 2024CodeRosetta: Pushing the Boundaries of Unsupervised Code Translation for Parallel Programmingcited by 19
  12. 2024Exploring the Capabilities of LLMs for Code Change Related Taskscited by 18
4 more
  1. 2024StackEval: Benchmarking LLMs in Coding Assistancecited by 15
  2. 2019How Different Are Different diff Algorithms in Git?cited by 6
  3. 2024DiffSpec: Differential Testing with LLMs using Natural Language Specifications and Code Artifactscited by 5
  4. 2017Parcels v0.9: prototyping a Lagrangian Ocean Analysis framework for the petascale agecited by 2

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