# Environment-in-the-Loop: Rethinking Code Migration with LLM-based Agents

arXiv: https://arxiv.org/abs/2602.09944 · HTML: https://mlsanity.com/paper/2602.09944/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 10 of its 27 references that are in the map.
- First posted on arXiv 10 Feb 2026.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 10 of 10)

- [The Rise and Potential of Large Language Model Based Agents: A Survey](https://mlsanity.com/paper/2309.07864/) (2023)
- [You Name It, I Run It: An LLM Agent to Execute Tests of Arbitrary Projects](https://mlsanity.com/paper/2412.10133/) (2024)
- [EnvBench: A Benchmark for Automated Environment Setup](https://mlsanity.com/paper/2503.14443/) (2025)
- [Beyond pip install: Evaluating LLM Agents for the Automated Installation of Python Projects](https://mlsanity.com/paper/2412.06294/) (2024)
- [Migrating Code At Scale With LLMs At Google](https://mlsanity.com/paper/2504.09691/) (2025)
- [DockerizeMe: Automatic Inference of Environment Dependencies for Python Code Snippets](https://mlsanity.com/paper/1905.11127/) (2019)
- [CODEMENV: Benchmarking Large Language Models on Code Migration](https://mlsanity.com/paper/2506.00894/) (2025)
- [CODESYNC: Synchronizing Large Language Models with Dynamic Code Evolution at Scale](https://mlsanity.com/paper/2502.16645/) (2025)
- [Challenges in migrating legacy software systems to the cloud an empirical study](https://mlsanity.com/paper/2004.10724/) (2020)
- [V2: Fast Detection of Configuration Drift in Python](https://mlsanity.com/paper/1909.06251/) (2019)

## Built on by (most cited first, 2 of 2)

- [Code as Agent Harness](https://mlsanity.com/paper/2605.18747/) (2026)
- [SkillGrad: Optimizing Agent Skills Like Gradient Descent](https://mlsanity.com/paper/2605.27760/) (2026)

## Abstract (arXiv)

Modern software systems continuously undergo code upgrades to enhance functionality, security, and performance, and Large Language Models (LLMs) have demonstrated remarkable capabilities in code migration tasks. However, while research on automated code migration which including refactoring, API adaptation, and dependency updates has advanced rapidly, the exploration of the automated environment interaction that must accompany it remains relatively scarce. In practice, code and its environment are intricately intertwined. Relying solely on static analysis of the environment leads to an inadequate understanding of the target setting, prolongs feedback cycles, and consequently causes significant rework and project delays, thereby reducing overall efficiency. We contend that successful software evolution demands a holistic perspective that integrates both code and environment migration. To understand the current landscape and challenges, we first provide an overview of the status of automated environment construction. We then propose a novel framework paradigm that tightly integrates automated environment setup with the code migration workflow. Finally, we explore the challenges and future directions for automated environment interaction within the code migration domain. Our findings emphasize that without automated environment interaction, the automation of code migration is only half complete.