# How is Google using AI for internal code migrations?

arXiv: https://arxiv.org/abs/2501.06972 · HTML: https://mlsanity.com/paper/2501.06972/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jan 2025.

## Built on by (most cited first, 6 of 6)

- [Migrating Code At Scale With LLMs At Google](https://mlsanity.com/paper/2504.09691/) (2025)
- [From Correctness to Collaboration: Toward a Human-Centered Framework for Evaluating AI Agent Behavior in Software Engineering](https://mlsanity.com/paper/2512.23844/) (2025)
- [A Multi-agent AI System for Deep Learning Model Migration from TensorFlow to JAX](https://mlsanity.com/paper/2603.27296/) (2026)
- [Understanding and supporting how developers prompt for LLM-powered code editing in practice](https://mlsanity.com/paper/2504.20196/) (2025)
- [APRIL: API Synthesis with Automatic Prompt Optimization and Reinforcement Learning](https://mlsanity.com/paper/2509.25196/) (2025)
- [Accuracy and Satisfaction in Multi-Turn LLM Dialogues for NFR Assessment](https://mlsanity.com/paper/2606.24834/) (2026)

## Abstract (arXiv)

In recent years, there has been a tremendous interest in using generative AI, and particularly large language models (LLMs) in software engineering; indeed there are now several commercially available tools, and many large companies also have created proprietary ML-based tools for their own software engineers. While the use of ML for common tasks such as code completion is available in commodity tools, there is a growing interest in application of LLMs for more bespoke purposes. One such purpose is code migration. This article is an experience report on using LLMs for code migrations at Google. It is not a research study, in the sense that we do not carry out comparisons against other approaches or evaluate research questions/hypotheses. Rather, we share our experiences in applying LLM-based code migration in an enterprise context across a range of migration cases, in the hope that other industry practitioners will find our insights useful. Many of these learnings apply to any application of ML in software engineering. We see evidence that the use of LLMs can reduce the time needed for migrations significantly, and can reduce barriers to get started and complete migration programs.