Environment-in-the-Loop: Rethinking Code Migration with LLM-based Agents
On the map: LLM agents and tool use → Software engineering coding agents
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.
Key facts. Cited by 2 papers in the map (data through 7 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.
Builds on
10 of 27 references.
Closest ancestors · 2
Same direction, newest first
- 2024You Name It, I Run It: An LLM Agent to Execute Tests of Arbitrary Projects13 Dec
- 2024Beyond pip install: Evaluating LLM Agents for the Automated Installation of Python Projects9 Dec
Other references · 8
- 2023The Rise and Potential of Large Language Model Based Agents: A Surveycited by 1,422
- 2025EnvBench: A Benchmark for Automated Environment Setupcited by 36
- 2025Migrating Code At Scale With LLMs At Googlecited by 14
- 2019DockerizeMe: Automatic Inference of Environment Dependencies for Python Code Snippetscited by 13
- 2025CODEMENV: Benchmarking Large Language Models on Code Migrationcited by 13
- 2025CODESYNC: Synchronizing Large Language Models with Dynamic Code Evolution at Scalecited by 8
- 2020Challenges in migrating legacy software systems to the cloud an empirical studycited by 3
- 2019V2: Fast Detection of Configuration Drift in Pythoncited by 2
Built on by
Citing papers per year: 2026: 2.
2 papers.
- 2026Code as Agent Harnesscited by 40
- 2026SkillGrad: Optimizing Agent Skills Like Gradient Descentcited by 23
- 2026SkillGrad: Optimizing Agent Skills Like Gradient Descent26 May 2026
- 2026Code as Agent Harness18 May 2026
Similar, not cited
Close in wording, no citation either way
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- 2026Code That Works, Environments That Don't: Measuring Environment Reproducibility in AI-Generated Software
- 2026Agent-Diff: Benchmarking LLM Agents on Enterprise API Tasks via Code Execution with State-Diff-Based Evaluation
- 2023Exploring Large Language Models in Resolving Environment-Related Crash Bugs: Localizing and Repairing
- 2026Refine After Generation: Toward Correct and Concise Patches in LLM-based Program Repair
- 2025FreshBrew: A Benchmark for Evaluating AI Agents on Java Code Migration
- 2026SWE-CI: Evaluating Agent Capabilities in Maintaining Codebases via Continuous Integration