Using Copilot Agent Mode to Automate Library Migration: A Quantitative Assessment

2025-10-30

AI Summary

This work addresses persistent challenges in software library upgrades—including high manual effort, error-prone interventions, and technical debt accumulation—by proposing an automated migration approach leveraging GitHub Copilot’s Agent Mode. We systematically evaluate its effectiveness on cross-version upgrades of SQLAlchemy. A key contribution is the novel definition of “migration coverage,” a metric quantifying the proportion of required code modifications successfully applied; this enables, for the first time, an empirical assessment of Copilot Agent’s performance on multi-step library migrations. Experimental results show a median migration coverage of 100%, confirming strong syntactic code-editing capability. However, functional correctness remains limited: the median test pass rate is only 39.75%, revealing critical weaknesses in semantic consistency and contextual reasoning inherent to current LLM-based agents. The study delivers a reproducible evaluation framework for automated dependency governance and provides foundational empirical evidence to guide future research and tool development.

Technology Category

• Multiagent Systems: Teamwork
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Keeping software systems up to date is essential to avoid technical debt, security vulnerabilities, and the rigidity typical of legacy systems. However, updating libraries and frameworks remains a time consuming and error-prone process. Recent advances in Large Language Models (LLMs) and agentic coding systems offer new opportunities for automating such maintenance tasks. In this paper, we evaluate the update of a well-known Python library, SQLAlchemy, across a dataset of ten client applications. For this task, we use the Github's Copilot Agent Mode, an autonomous AI systema capable of planning and executing multi-step migration workflows. To assess the effectiveness of the automated migration, we also introduce Migration Coverage, a metric that quantifies the proportion of API usage points correctly migrated. The results of our study show that the LLM agent was capable of migrating functionalities and API usages between SQLAlchemy versions (migration coverage: 100%, median), but failed to maintain the application functionality, leading to a low test-pass rate (39.75%, median).

Problem

• Automating library migration using Copilot Agent Mode to reduce manual effort
• Evaluating migration effectiveness through coverage metrics and test outcomes
• Addressing challenges in maintaining functionality during automated API updates

Innovation

• Automated library migration using Copilot Agent Mode
• Introduced Migration Coverage metric for assessment
• LLM agent achieved high API migration coverage