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

2026-02-10

AI Summary

This work proposes a novel “Environment-in-the-Loop” paradigm that systematically integrates automated environment construction with code migration, addressing the common oversight in existing approaches of neglecting dynamic interaction with the target runtime environment. By leveraging large language model (LLM) agents to drive the co-evolution of code and its execution environment, the method combines static and dynamic environment analysis, API adaptation, and dependency resolution to significantly enhance both the accuracy and efficiency of code migration. The study demonstrates that joint migration of environment and code is not only beneficial but essential, offering a new pathway toward fully automated software evolution.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Multiagent Systems: Agent-Based Simulation and Emergent Behavior
• Search and Optimization: Evolutionary Computation

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Web Mining and Content Analysis: Models for Web evolution
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

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.

Problem

• code migration
• environment interaction
• automated environment
• software evolution
• LLM-based agents

Innovation

• Environment-in-the-Loop
• code migration
• LLM-based agents
• automated environment interaction
• software evolution