Smart Code Refactoring with AI Agents
Automating Haskell code improvement through multi-agent LLM systems
This research introduces a specialized multi-agent system that leverages large language models to automatically refactor and improve Haskell codebases.
- 13.64% reduction in cyclomatic complexity through AI-driven refactoring
- Distributed approach with specialized agents for context analysis, refactoring, validation, and testing
- Comprehensive evaluation using metrics like run-time and memory allocation
- Practical demonstration of how LLMs can collaborate in a multi-agent system for complex software engineering tasks
This research matters for engineering teams seeking to automate code quality improvements, reduce technical debt, and optimize performance in functional programming environments.