AI BriefingClaude Code Refactoring Benchmark
Key point
Claude Code with MCP code health guidance increased refactoring improvements by 2-5x.
Details
Claude Code's legacy refactoring was compared using a public dataset containing 25,000 competitive programming source codes and unit tests.
- Correctness was verified with unit tests, and code health was measured with CodeScene.
- Unguided runs mostly produced shallow edits like variable renaming.
- Runs with MCP code health guidance showed a significant increase in structural refactoring such as method extraction and separation of responsibilities.
As a result, guided Claude Code showed a 2-5x larger improvement in Code Health compared to unguided runs.
The lower the code health, the less stable the results were, with defect risk rising to 60%+ on unhealthy code. Conversely, AI operated more stably once Code Health reached a level of 9.5/10.0.
This shows that the quality of legacy code itself is a bottleneck that determines the performance of AI-assisted development.
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