Revolutionizing Software Quality: AI-Driven Advanced Code Refactoring and Developer Growth

Abstract

In the rapidly evolving landscape of software development, maintaining high-quality, efficient, and maintainable code has become more critical than ever. Traditional code refactoring techniques, while effective, often require significant manual effort, leading to increased development time and technical debt. This paper explores how artificial intelligence (AI)-driven code refactoring is revolutionizing software quality by automating optimizations, identifying anti-patterns, and suggesting best practices in real time. By leveraging machine learning models, AI-assisted tools can enhance code readability, performance, and security while reducing errors. Furthermore, this paper examines how AI-driven refactoring fosters developer growth by providing intelligent insights, personalized recommendations, and continuous learning opportunities.

Introduction

Overview

The research introduces a novel methodology leveraging Large Language Models (LLMs) to improve software development efficiency and code quality. The proposed LLM-based model is trained on large code repositories to:

• Detect code smells

• Identify bugs

• Suggest refactoring and improvements

• Promote coding best practices

This AI-powered solution serves a dual purpose: improving post-release code quality and educating developers through actionable feedback.

Key Components of the System

1. Multi-Agent Architecture

The system employs a modular, three-agent pipeline, each handling a specialized task:

• SyntaxAgent: Fixes syntax errors

• CodeSmellDetectionAgent: Identifies poor design/code patterns

• CodeEnhancementAgent: Refactors code for performance, readability, and best practices

Each agent uses instruction-tuned LLMs (like LLaMA, Mistral), trained with task-specific prompts in Alpaca-style format.

2. Model Training and Fine-Tuning

• Fine-tuning tools: Used LoRA (Low-Rank Adaptation) and Unsloth for efficient training on limited hardware

• Datasets: Buggy and fixed code samples from Python and Java (~43,000 rows)

• Preprocessing: Removal of duplicates, handling nulls, standardizing input formats

3. Prompt Engineering

Prompts were carefully designed with three sections:

• Instruction (what to do)

• Input (buggy/incomplete code)

• Expected Output (corrected/refactored version)

This ensured clarity of intent and improved model precision during both training and inference.

4. Natural Language Processing Techniques Used

The system uses advanced NLP techniques to understand and process code:

• Tokenization: Breaks code into understandable units (keywords, variables, operators)

• Embeddings: Maps tokens to high-dimensional vectors to retain semantic context

• Self-Attention: Captures long-range dependencies (e.g., variable reuse across lines)

• Contextual Understanding: Preserves code logic during transformations

5. Features of the LLM System

• Pattern Matching: Recognizes standard programming patterns and naming conventions

• Context-Aware Refactoring: Enhances code while preserving logic and improving clarity

• Modular & Scalable: Each agent handles one task, enabling easier debugging and system updates

• Lightweight Deployment: Optimized for consumer-grade GPUs using quantized models

6. Model Evaluation

Metrics used to evaluate performance:

• Accuracy, Precision, Recall, F1-Score (for syntax and bug detection)

• BLEU, ROUGE-L, Word Error Rate (for code generation quality)

• Maintainability Index (for structural improvements)

7. Future Directions

The research plans to:

• Compare LLM-generated documentation against manual updates

• Conduct empirical evaluations using manual code reviews

• Investigate developer sentiment on LLM feedback

• Improve integration with developer communities and forums

Conclusion

This project presents a novel, agent-based approach to automated code refactoring using Large Language Models (LLMs). By segmenting the process into specialized agents—Syntax Agent, Code Smell Detection Agent, and Code Enhancement Agent—we successfully addressed core software engineering objectives such as simplifying complex code, enforcing naming conventions, modernizing syntax, improving exception handling, and automating repetitive tasks. Through the use of instruction-tuned LLMs like LLaMA 3 and Mistral, enhanced with LoRA-based fine-tuning and carefully engineered prompts, the system demonstrated strong performanceinreal-worldcodecorrectionandenhancementscenarios.EvaluationmetricssuchasMaintainabilityIndexandROUGE- L supported the system’s effectiveness, even where traditional NLP metrics showed limitations. Overall, thismodular architecture notonlyimproves codequality and maintainability but also showcases how LLMs can be harnessed for intelligent, context-aware software engineering tasks. The approach opens the door for future work in integrating more advanced agents, real-time feedback mechanisms, and deployment into real-world development environments.

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Copyright

Copyright © 2025 Prof. Sowmya N, Mr. Prajwal K P, Mr. Sudhamshu H, Mr. Abhishek Gowda P. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.