How to Safely Refactor AI-Generated Code Using Automated Test Harnesses

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AI coding models frequently introduce technical debt by duplicating logic, suppressing errors, and installing unnecessary dependencies when fulfilling user prompts. ZeroLabs has published a pragmatic framework for cleaning up such AI-generated codebases before they reach production. The approach centers on a four-step refactoring loop — dead code removal, type hardening, utility deduplication, and performance tuning — each validated by automated tests. Engineers are advised to write smoke and contract tests first, then restrict AI agents to specific directories while keeping test files immutable. Static analysis tools like knip and vulture are recommended to detect unused imports and orphaned functions systematically.

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