Best AI tools for refactoring legacy code

What this is for

Refactoring legacy code means updating outdated software to improve maintainability, efficiency, and compatibility with current systems. This typically involves reviewing and rewriting code to eliminate technical debt, improve readability, and reduce complexity. Manual refactoring is slow and error-prone—easy to introduce bugs, get lost in details, or simply run out of resources before finishing.

What to look for in a tool

When evaluating refactoring tools, consider:

• Code analysis: Can it identify areas needing work—duplicated code, dead code, performance bottlenecks?
• Language support: Does it handle the languages and frameworks in your codebase?
• Workflow integration: Can it plug into your IDE, version control, and CI pipeline?
• Automated suggestions: Does it recommend specific changes like variable renames, function extraction, or conditional simplification?
• Code quality checks: Does it enforce standards, catch common bugs like null pointer exceptions, and flag anti-patterns?

Common pitfalls

When selecting and using a refactoring tool, watch for:

• Over-reliance on automation: Automated suggestions can miss context or produce incomplete changes. Always review before committing.
• Insufficient testing: Refactored code needs thorough testing. New bugs slip in easily.
• Ignoring broader context: Refactoring in isolation risks breaking dependencies or creating unintended side effects elsewhere in the codebase.

Below are AI tools that handle refactoring legacy code in different ways—pick based on your stack and the criteria above.