LLM-based code quality improvement, automated refactoring, and code review suggestions.

Abstract

Problem: Large Language Models (LLMs) have revolutionized software engineering by automating tasks such as code quality improvement, refactoring, and code review suggestions. Despite these advancements, current tools face significant limitations in generalizability, safety, and evaluation. They are often tailored to specific programming languages (e.g., Java, Python) and lack robust mechanisms to ensure the safety and reliability of refactoring outputs (Pomian et al., 2024). Moreover, the absence of standardized evaluation metrics complicates their benchmarking, hindering their scalability and effectiveness in diverse, multi-language environments (Wadhwa et al., 2023; Liu et al., 2024). Methodology: This research introduces a generalized LLM-based framework designed to overcome these challenges by enabling automated code quality improvement, refactoring, and code review suggestions across multiple programming languages. The framework integrates fine-tuned LLMs (e.g., GPT-4, StarCoder2) trained on diverse datasets such as Ref-Dataset and Java refactoring commits (Yu et al., 2024; Cordeiro et al., 2024). Safety mechanisms, such as RefactoringMirror, are incorporated to validate outputs and prevent unsafe changes (Liu et al., 2024). Standardized evaluation metrics for accuracy, scalability, and generalizability are utilized to benchmark performance (Finkman et al., 2024). The framework development adheres to Object-Oriented Analysis and Design (OOAD) principles and employs Agile methodologies for iterative refinement and adaptability.