Shahbaz Siddeeq

Abstract:

In Software Engineering (SE), functional programming languages like Haskell are known for their readability and predictability, but they also present unique challenges during code refactoring due to immutability and lazy evaluation. Traditional tools fo- cus on syntax adjustments rather than deeper structural improvements, leaving a gap in automated solutions that can efficiently refactor functional codebases. Large Lan- guage Models (LLMs) have transformed code generation in object-oriented languages, but their potential for refactoring functional programming languages remains underex- plored. We propose a LLMs based multi-agent system to automate the refactor process of functional programming languages, with a focus on Haskell. The proposed system will utilize specialized agents to tackle different refactoring tasks while preserving the program’s behavior and improving performance. Each agent, will optimize for various phases of the refactoring process, will work collaboratively to manage legacy code- bases and apply refinements with minimal human intervention. We aim to enhance the efficiency of refactoring, reduce the risk of errors, and extend these benefits to large, complex functional codebases that have traditionally been difficult to modernize.

Motivation:

Research Questions:

• RQ1: How can LLMs optimize the refactoring of functional programming languages compared to traditional manual methods?
• RQ2: What performance improvements can LLM-based refactoring achieve in functional languages as compared to object-oriented languages?
• RQ3: How data structures in functional programming can improve Rafactoring process?

Challenges:

• Context Sensitivity: Maintaining context during the refactoring of functional languages is challenging because of their non-sequential execution and lazy eval- uation [11]. We will use LLMs to ensure code consistency and reduce errors during the refactoring process
• Scalability: Scaling LLM-based refactoring to handle large codebases creates considerable computational challenges. We will distribute tasks across multi- ple agents, reducing the computational load while maintaining speed and effi- ciency [13].
• Handling Functional Programming Paradigms: LLMs usually perform well with object-oriented languages, but adapting them to functional languages, which emphasize mathematical precision and immutability, remains challenging. Train- ing datasets will be include with examples from functional programming to im- prove LLM performance in this area [17].

Multi-agent System:

Expected Contributions:

This research will introduce a new approach that integrates LLMs with multi-agent systems to improve refactoring in functional programming. It will expand the under- standing of AI applications in functional languages and provide a new framework for automated refactoring.

The research will result in an open-source tool that automates the refactoring process for functional languages like Haskell, addressing both the technical debt and maintain- ability issues that arise in long-term software development.

It will contribute to academic discourse through publications in software engineering and AI conferences, providing insights into the potential for multi-agent systems in automating complex programming tasks.

Expected Publications:

• Article 1: Closed LLMs based mutli-agent system for refactoring Functional language codebases – RQ1
  • We will study how Large Language Models (LLMs) can optimize the refactoring process for functional programming languages compared to traditional manual methods. I will explore the specific mechanisms through which LLMs improve the accuracy and efficiency of refactoring. This study will help identify key ben- efits of automation in functional language contexts.
• Article2:ComparativeAnalysisofFine-TunedTinyOpen-SourceLLMsand Closed-Source LLMs in Multi-Agent Refactoring Systems – RQ1
• Article 3: Performance Improvements in LLM-Based Refactoring: Func- tional vs. Object-Oriented Paradigms – RQ2
  • We will investigate the performance differences achieved through LLM-based refactoring in functional languages compared to object-oriented languages. We will evaluate various performance metrics, such as code efficiency and error re- duction, to demonstrate how refactoring outcomes vary between these paradigms.
• Article 4: Enhancing Refactoring through Data Structures in Functional Programming – RQ3
  • The focus of this paper will be on how data structures in functional program- ming can enhance the refactoring process. We will examine how LLMs han- dle functional language-specific data structures to optimize refactoring strategies, providing a deeper understanding of their role in improving code consistency and efficiency.
• Article 5: Addressing Challenges in LLM-Based Refactoring for Functional Programming – RQ1,RQ3
  • We will address key challenges associated with LLM-based refactoring for func- tional programming languages, including context sensitivity, scalability, and adapting to functional paradigms. We will focus on using LLMs to maintain con- text during refactoring, distributing tasks across multiple agents for scalability, and improving LLM performance with functional programming-specific training datasets.

Research Interests:

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