Distributed Approach to Haskell Based Applications Refactoring with LLMs Based Multi-Agent Systems

TL;DR

This paper introduces a multi-agent system utilizing large language models to automate and optimize Haskell code refactoring, significantly reducing complexity and improving performance metrics across codebases.

Contribution

It presents a novel distributed multi-agent framework leveraging LLMs for automated Haskell code refactoring, focusing on metrics like complexity, memory, and runtime.

Findings

Cyclomatic complexity reduced by up to 47.06%

Memory allocation improved by up to 41.73%

Runtime efficiency increased by up to 50%

Abstract

We present a large language models (LLMs) based multi-agent system to automate the refactoring of Haskell codebases. The multi-agent system consists of specialized agents performing tasks such as context analysis, refactoring, validation, and testing. Refactoring improvements are using metrics such as cyclomatic complexity, run-time, and memory allocation. Experimental evaluations conducted on Haskell codebases demonstrate improvements in code quality. Cyclomatic complexity was reduced by 13.64% and 47.06% in the respective codebases. Memory allocation improved by 4.17% and 41.73%, while runtime efficiency increased by up to 50%. These metrics highlight the systems ability to optimize Haskells functional paradigms while maintaining correctness and scalability. Results show reductions in complexity and performance enhancements across codebases. The integration of LLMs based multi-agent system enables precise task execution and inter-agent collaboration, addressing the challenges of refactoring in functional programming. This approach aims to address the challenges of refactoring functional programming languages through distributed and modular systems.