Copilot Agent Mode in Code Migration

• Copilot Agent Mode is an autonomous system that automates multi-step code migrations using large language models.
• The system orchestrates high-level planning, environment setup, code transformation, and testing to achieve library migrations like SQLAlchemy upgrades.
• Empirical evaluations reveal high syntactic coverage but highlight challenges in semantic correctness and dependency complexities post-migration.

Copilot Agent Mode refers to a class of autonomous, LLM-driven systems capable of planning and executing multi-step workflows in tasks that traditionally require extensive software engineering expertise, such as upgrading dependencies and refactoring code in software maintenance. This concept is exemplified in the context of library migration, where Copilot Agent Mode demonstrates the ability to autonomously perform functionally complex updates—such as migrating client applications from one major version of a library to another—leveraging both high-level planning and low-level code transformation.

1. Definition and System Overview

Copilot Agent Mode, as instantiated by Github's Copilot platform, is an autonomous LLM system capable of multi-step codebase migration and maintenance operations. Unlike conventional chat-based or prompt-based LLM interfaces, the agentic approach in Copilot Agent Mode coordinates (i) instruction synthesis, (ii) codebase modification, (iii) dependency and environment management, and (iv) iterative build and test procedures, systematically orchestrated with minimal human intervention.

The paradigm features:

• Autonomous Workflow Execution: Planning and carrying out sequential actions across editing, testing, and environment setup.
• Minimal Human-in-the-Loop: Engineered for end-to-end automation; human responses are limited to trivial prompts or failure recovery, if enabled.
• Prompt-Driven Scripting: Formalizes high-level migration workflows via engineered prompts for consistency and reproducibility.
• Integration with Project Ecosystem: Directly interacts with codebases, dependency managers (e.g., pip), and test runners.

This architecture enables consistent, repeatable software transformations without granular prompting or stepwise human approval.

2. Migration Workflow and Experimental Methodology

For the assessment of Copilot Agent Mode in library migration (Almeida et al., 30 Oct 2025), the methodology consists of two principal stages:

1. Instruction Generation: The system generates a migration instruction document (e.g., migrate-sqlalchemy.instructions.md) for the target upgrade (SQLAlchemy 1.x to 2.x). This step leverages prompt engineering referencing official migration guides, code examples, and previous transformation rules.
2. Automated Execution: Utilizing the engineered instructions, the agent:
   • Instantiates an isolated Python environment.
   • Upgrades the specified library via the appropriate package manager.
   • Edits the codebase according to the migration instructions.
   • Compiles and runs tests, capturing outcomes.

The agent is instructed through “one-shot” prompts to ensure uniform evaluation and to eliminate variability due to LLM non-determinism. Interaction is strictly limited: any required human inputs (e.g., confirming workflow continuation) are provided as boilerplate; unrecoverable errors or infinite loops lead to workflow termination.

This methodology was empirically evaluated on ten real-world repositories, each with pre-existing passing test suites and complete SQLAlchemy 1.x integrations.

3. Metrics for Automated Migration Evaluation

Effectiveness was quantified using a suite of metrics designed to capture both technical correctness and application-level quality:

• Migration Coverage: Proportion of API usage sites correctly migrated. Computation follows:

$$\text{Migration Coverage} = \frac{\sum \text{Correct Transformations}}{\sum \text{Instances Needing Transformation}}$$

All required transformation rules (e.g., replacing Column with mapped_column) were enumerated, and migration outcomes were manually validated per usage site.

• Test Suite Pass Rate: Pre- and post-migration execution of project test suites, measuring logical and behavioral integrity.
• Compilation Success: Binary indicator of whether the post-migration code compiles/executable.
• Code Quality and Type Safety: Delta in Pylint (code style/errors) and Pyright (type system violations) metrics.

All metrics were reported both in aggregate and as project-wise medians to capture distributional effects.

4. Quantitative Results and Empirical Findings

The paper's central findings are summarized below:

Metric	Before Migration	After Migration
Migration Coverage (Aggregate)	—	45.48%
Migration Coverage (Median)	—	100%
Passing Tests (Aggregate)	87.84%	53.61%
Passing Tests (Median)	100%	39.75%
Compiling Repositories	10	8
Average Pylint Score	6.16	6.48
Average Pyright Errors	45.8	35.6

Additional breakdown:

• 5/10 repositories achieved 100% migration coverage and >80% test pass rates; 8/10 achieved >80% coverage.
• Only 2/10 repositories passed all tests post-migration.
• Failures were not exclusively due to incomplete migration, but also to application-level regressions, dependency breakages, and semantic mismatches.
• Code quality and type safety saw modest improvements overall, reflecting more up-to-date idioms and stricter typing in migrated code.

5. Effectiveness, Limitations, and Comparative Analysis

Effectiveness

• High Migration Fidelity: Copilot Agent Mode achieved comprehensive migration in 80% of projects at the syntactic level. Half of these yielded functionally sound software.
• Superior to Prompt-Only Approaches: Agent Mode demonstrably outperformed non-agentic, prompt-based LLM methods on migration accuracy, coverage, and code quality for the same migration task.
• End-to-End Automation: The system executed full migrations with minimal manual oversight, effectively demonstrating agentic autonomy for well-documented upgrade paths.

Limitations

• Functional Correctness Gap: High migration coverage does not guarantee post-migration application correctness; observed test pass rates are significantly lower than coverage rates.
• Ecosystem and Dependency Complexity: Failures often stem from unresolved dependencies, asynchronous execution requirements, or library incompatibilities (as observed with packages such as casbin). These cases confound the agent's ability to achieve successful, compiling builds.
• Partial/Failed Migrations: Incomplete transformations can produce code that fails to compile. Human-in-the-loop intervention or more sophisticated error recovery strategies may be needed.
• Task Narrowness: Experimental scope limited to a specific library and programming language (SQLAlchemy, Python). Generalizability to other migration scenarios or languages is not established.
• Lack of Semantic Awareness: The agent operates primarily at the code transformation level, lacking deep semantic or behavioral reasoning about application-specific logic, resulting in latent bugs or altered application semantics.

6. Broader Implications and Recommendations

Agentic LLMs in Copilot Agent Mode represent a substantial advance for automating large-scale, repetitive, and precisely-specified maintenance tasks, such as library migrations with clear official documentation. However, fully autonomous migration remains insufficient for comprehensive software maintainability in production settings due to the disconnect between syntactic transformation and semantic preservation.

A plausible implication is that inclusion of human-in-the-loop workflows and runtime feedback (e.g., dynamic test oracle integration, targeted error logging, guided dependency recovery) will be necessary for closing the correctness and reliability gap. Incorporating richer execution feedback and more granular handling of context-specific application semantics presents a logical next step.

7. Summary Table: Efficacy and Tradeoffs

Strengths	Weaknesses
High syntactic coverage	Low median test pass rates post-migration
Autonomous, minimal manual overhead	Incomplete handling of complex dependencies
Improved code style/type safety	Non-guaranteed semantic correctness
Outperforms prompt-based approaches	Generalizability outside SQLAlchemy/Python not shown

8. Conclusion

Copilot Agent Mode delivers high performance on syntactic aspects of library migration and demonstrates meaningful improvements in agentic automation across software engineering workflows. Persistent challenges remain regarding application-level correctness, graceful recovery from ecosystem-level failures, and the extension of these capabilities to domains with less structured or poorly-documented migration processes. Future work should focus on hybrid agent-human migration pipelines, deeper integration of semantic analysis, and broader evaluations across languages and libraries to establish robustness and domain-transcending applicability.