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

Xiang Li, Zhiwei Fei, Ying Ma, Jerry Zhang, Sarro Federica, He Ye

arXiv:2602.09944v1cs.SE

TL;DR

Code migration research has underexplored the automated environment interaction needed to validate migrated systems under real runtime conditions. The paper surveys this gap and proposes an environment-driven, multi-agent workflow integrating environment setup, migration, testing, and feedback. It concludes that environment interaction is necessary for end-to-end migration automation, while environment reproduction, dependency management, coordination, and industrial integration remain open challenges.

  • Problem

    Current migration workflows separate code modification from dynamic environment configuration, allowing runtime incompatibilities and manual validation to cause rework.

    Source: §1 · pp. 1–2, §2.1 · p. 2

  • Method

    The paper proposes a three-agent environment-driven workflow that couples migration planning with automated environment construction, testing, diagnostics, and iterative feedback.

    Source: §1 · p. 2, §2.3 · p. 3, §3 · p. 3

  • Results

    The proposed paradigm provides end-to-end automation from migration analysis through environment construction, testing, and iterative repair.

    Source: §1 · p. 2, §3.2 · p. 4

  • Takeaways & Limitations

    Code migration should treat source code, dependencies, and execution environments as co-evolving components integrated with continuous CI/CD feedback.

    Source: §5 · p. 4

    Environment reproduction, dependency management, multi-agent feedback coordination, and industrial CI/CD integration remain unresolved challenges.

    Source: §4 · p. 4

Abstract

from arXiv · show

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.

1 Introduction

Code migration spans interdependent code, dependencies, and execution environments, but current workflows separate code modification from environment configuration. The paper proposes an LLM-based environment-driven framework that uses dynamic feedback to guide migration and reduce rework.

  • Repository-scale migration requires coordinated changes across code, dependencies, schemas, and deployment systems, making a stable post-migration environment essential.Source: §1 · p. 1
  • Static analysis misses subtle version-dependent runtime errors and treats the environment as immutable, causing rework and project delays.Source: §1 · p. 2
  • LLMs are mainly used for interactive code editing, while poor execution-outcome prediction motivates automated environment setup, execution, and testing.Source: §1 · p. 2
  • The proposed framework creates a dynamic feedback loop that returns test failures and runtime behavior to code migration, enabling earlier problem resolution and less rework.Source: §1 · p. 2

2 Background

The background identifies three connected gaps: migration is decoupled from execution environments, validation remains manual, and LLM-based environment interaction lacks a systematic paradigm. Existing environment agents and industrial tools show relevant capabilities, but the paper targets their integration with code migration.

2.1 Challenges

  • Static environment analysis cannot detect subtle version-dependent runtime errors, while manual setup, dependency resolution, and testing create rework and leave dynamic feedback absent.Source: §2.1 · p. 2

2.2 Automated Environment Interaction

  • Environment-construction agents combine planning, memory, perception, and action, and existing systems demonstrate repository setup across languages and benchmarked configuration challenges.Source: §2.2 · p. 2

2.3 LLM-assisted Code Migration

  • LLMs can understand project structure and environmental constraints, but commonly act as advisors rather than executors while target environments remain unknown or dynamically generated.Source: §2.3 · p. 3
  • Industrial sandboxes and CI integrations increasingly connect code changes, environment execution, and runtime validation into iterative improvement loops.Source: §2.3 · p. 3
  • Existing research has not established a systematic agent paradigm that actively builds environments and interacts with them throughout code migration.Source: §2.3 · p. 3

3 Environment-driven Multi-Agent Workflow

The proposed workflow uses three collaborating agents in a continuous loop of migration planning, environment setup, test validation, and feedback refinement. The Environment Agent serves as the verification hub, connecting executable environments, diagnostics, testing, repair, and CI/CD revalidation.

3 Environment-driven Multi-Agent Workflow

  • The workflow synchronizes Migration, Environment, and Testsuite Agents across planning, environment setup, test validation, and feedback refinement.Source: §3 · p. 3, §3.1 · p. 3

3.1 Agent Components

  • The Migration Agent modifies code and dependency plans, the Environment Agent constructs executable environments and diagnostics, and the Testsuite Agent generates and improves validation tests.Source: §3.1 · pp. 3–4
  • Reproducible and traceable build environments make migration a dynamic, environment-aware cycle across dependency and configuration drift.Source: §3.1 · p. 3

3.2.1 Task Trigger and Migration Planning.

  • CI/CD triggers migration planning when upgrades or dependency changes are detected, after which the Environment Agent provisions runtimes, resolves dependencies, configures build systems, and executes the project in isolation.Source: §3.2.1 · p. 4, §3.2.2 · p. 4

3.2.2 Automated Environment Interaction.

  • Environment failures produce diagnostics that route configuration fixes to the Environment Agent, semantic fixes to the Migration Agent, and behavioral anomalies to the Testsuite Agent.Source: §3.2.2 · p. 4

3.2.3 Test Generation and Execution.

  • Regression tests run in the verified environment while logs, system states, and contextual traces anchor iterative correction and continuous coordination.Source: §3.2.3 · p. 4, §3.2.4 · p. 4

3.2.4 Feedback Aggregation and CI/CD Integration.

  • Validated code, configurations, tests, coverage, and logs are uploaded as CI/CD artifacts, enabling revalidation after upgrades or dependency and configuration drift.Source: §3.2.4 · p. 4

4 Challenges and Further Directions

The paper identifies environment setup reliability, dependency reproduction, and coordination between environment and migration agents as open challenges. It also emphasizes integrating environment construction with CI/CD workflows and developer auditing.

4 Challenges and Further Directions

  • Reproducing a runnable environment remains difficult because systems require accurate configuration-drift detection and robust dependency inference.The paper proposes learning environment semantics from build scripts, runtime traces, or CI logs to improve reproducibility and portability.Source: §4 · p. 4
  • Environment feedback such as build errors and missing libraries must be interpreted to trigger meaningful fixes rather than simple retries.Reinforcement learning and cooperative multi-agent systems are suggested for more stable correction cycles.Source: §4 · p. 4
  • Industrial adoption requires automated environment construction to align with CI/CD workflows through sandbox creation, reconstruction logging, and developer auditing.Environment Agents could become transparent extensions to container orchestration systems such as Docker or Kubernetes.Source: §4 · p. 4

5 Conclusion

The conclusion argues that automated environment setup should be a first-class part of code migration. It frames migration as the co-evolution of source code, dependencies, and execution environments within CI/CD workflows.

5 Conclusion

  • Migration systems should treat automated environment setup as a first-class concern because correctness is verified within a runnable environment.The conclusion contrasts this position with research and tools that predominantly focus on source code.Source: §5 · p. 4
  • The proposed paradigm couples source-code migration with dependency and execution-environment evolution through continuous feedback from Migration and Testsuite Agents.Source: §5 · p. 4
  • Embedding environment-aware migration into CI/CD workflows is presented as a path toward sustaining long-term software evolution across ecosystems.Source: §5 · p. 4

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