# LLM-based Multi-Agent System for Intelligent Refactoring of Haskell Code

arXiv: https://arxiv.org/abs/2506.19481 · HTML: https://mlsanity.com/paper/2506.19481/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 15 of its 42 references that are in the map.
- First posted on arXiv 24 Jun 2025.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 15 of 15)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Large Language Model based Multi-Agents: A Survey of Progress and Challenges](https://mlsanity.com/paper/2402.01680/) (2024)
- [A Survey on Large Language Models for Code Generation](https://mlsanity.com/paper/2406.00515/) (2024)
- [IntelliCode Compose: Code Generation Using Transformer](https://mlsanity.com/paper/2005.08025/) (2020)
- [A Survey of Machine Learning for Big Code and Naturalness](https://mlsanity.com/paper/1709.06182/) (2017)
- [War and Peace (WarAgent): Large Language Model-based Multi-Agent Simulation of World Wars](https://mlsanity.com/paper/2311.17227/) (2023)
- [Exploring Large Language Model based Intelligent Agents: Definitions, Methods, and Prospects](https://mlsanity.com/paper/2401.03428/) (2024)
- [ChatGPT Prompt Patterns for Improving Code Quality, Refactoring, Requirements Elicitation, and Software Design](https://mlsanity.com/paper/2303.07839/) (2023)
- [Self-Adaptive Large Language Model (LLM)-Based Multiagent Systems](https://mlsanity.com/paper/2307.06187/) (2023)
- [Refactoring Programs Using Large Language Models with Few-Shot Examples](https://mlsanity.com/paper/2311.11690/) (2023)
- [Levels of AI Agents: from Rules to Large Language Models](https://mlsanity.com/paper/2405.06643/) (2024)
- [Refactoring Practices in the Context of Modern Code Review: An Industrial Case Study at Xerox](https://mlsanity.com/paper/2102.05201/) (2021)
- [Genetic Auto-prompt Learning for Pre-trained Code Intelligence Language Models](https://mlsanity.com/paper/2403.13588/) (2024)
- [Fixing Your Own Smells: Adding a Mistake-Based Familiarisation Step When Teaching Code Refactoring](https://mlsanity.com/paper/2401.01011/) (2024)

## Built on by (most cited first, 3 of 3)

- [CATArena: Evaluating Evolutionary Capabilities of Code Agents via Iterative Tournaments](https://mlsanity.com/paper/2510.26852/) (2025)
- [AI-Assisted Unit Test Writing and Test-Driven Code Refactoring: A Case Study](https://mlsanity.com/paper/2604.03135/) (2026)
- [RefactorPlatform: An Open-Source Harness for Controlled Evaluation of Repository-Scale Refactoring Agents](https://mlsanity.com/paper/2609.04898/) (2026)

## Abstract (arXiv)

Refactoring is a constant activity in software development and maintenance. Scale and maintain software systems are based on code refactoring. However, this process is still labor intensive, as it requires programmers to analyze the codebases in detail to avoid introducing new defects. In this research, we put forward a large language model (LLM)-based multi-agent system to automate the refactoring process on Haskell code. The objective of this research is to evaluate the effect of LLM-based agents in performing structured and semantically accurate refactoring on Haskell code. Our proposed multi-agent system based on specialized agents with distinct roles, including code analysis, refactoring execution, verification, and debugging. To test the effectiveness and practical applicability of the multi-agent system, we conducted evaluations using different open-source Haskell codebases. The results of the experiments carried out showed that the proposed LLM-based multi-agent system could average 11.03% decreased complexity in code, an improvement of 22.46% in overall code quality, and increase performance efficiency by an average of 13.27%. Furthermore, memory allocation was optimized by up to 14.57%. These results highlight the ability of LLM-based multi-agent in managing refactoring tasks targeted toward functional programming paradigms. Our findings hint that LLM-based multi-agent systems integration into the refactoring of functional programming languages can enhance maintainability and support automated development workflows.