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From Cosmetic to Structural: A Paired Experiment on Metric-Guided Agentic Refactoring

Source
arxiv.org
Author
Nadim Hagatulah, Markus Borg, Emma S\"oderberg
Date
Why it matters

Giving a coding agent code-health feedback through MCP shifts it from cosmetic to structural refactors, but below roughly CodeHealth 5 to 7 it can cause regressions. That tells you when guided refactoring is safe to automate.

Key takeaways · AI-distilled
  • Per the abstract, giving the gptme coding CodeScene code-smell reports and CodeHealth scores through an server raised the rate of effective refactorings by 21 to 26 percentage points versus unguided runs.
  • The study ran a paired experiment on 24,620 single-file competitive programming solutions in C++, Java, and Python, comparing guided and unguided refactorings of the same code.
  • The authors report that guidance pays off only above language-specific break-even points of CodeHealth 4.9 to 7.1; below them, the structural edits it encourages increasingly risk regressions.
  • Guided runs cost more: about 3 to 5 times the over roughly twice as many turns, and they shifted failures toward logical test failures and introduced new coupling smells.
Terms in this piece · Glossary
  • AI agent — An AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.
  • MCP — The Model Context Protocol — an open standard that lets any AI assistant plug into any tool or data source without custom integration code.
  • token — The chunk of text a model reads and writes in — roughly three-quarters of a word — and the unit AI usage is billed in.
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