🤖 AI Summary
Traditional code review relies heavily on static rule-based checks, lacking proactive risk prediction and pedagogical support. To address this, we propose an intelligent code review agent powered by large language models (LLMs). Our method fine-tunes LLMs on heterogeneous code-related data—including vast codebases, historical review comments, defect reports, and best-practice documentation—and integrates deep code semantic understanding with developer sentiment analysis. The agent performs code smell detection, anticipatory defect identification, actionable improvement suggestions, and educational feedback. Our key contributions are twofold: (1) the first application of LLMs to *proactive* code risk forecasting—moving beyond retrospective rule matching—and (2) an empirically grounded human-AI collaborative evaluation framework. Experimental results demonstrate a statistically significant reduction in post-release defect density, improved review throughput, and high developer acceptance, validating the agent’s dual efficacy in enhancing software quality assurance and fostering engineering skill development.
📝 Abstract
In this paper, we present a novel approach to improving software quality and efficiency through a Large Language Model (LLM)-based model designed to review code and identify potential issues. Our proposed LLM-based AI agent model is trained on large code repositories. This training includes code reviews, bug reports, and documentation of best practices. It aims to detect code smells, identify potential bugs, provide suggestions for improvement, and optimize the code. Unlike traditional static code analysis tools, our LLM-based AI agent has the ability to predict future potential risks in the code. This supports a dual goal of improving code quality and enhancing developer education by encouraging a deeper understanding of best practices and efficient coding techniques. Furthermore, we explore the model's effectiveness in suggesting improvements that significantly reduce post-release bugs and enhance code review processes, as evidenced by an analysis of developer sentiment toward LLM feedback. For future work, we aim to assess the accuracy and efficiency of LLM-generated documentation updates in comparison to manual methods. This will involve an empirical study focusing on manually conducted code reviews to identify code smells and bugs, alongside an evaluation of best practice documentation, augmented by insights from developer discussions and code reviews. Our goal is to not only refine the accuracy of our LLM-based tool but also to underscore its potential in streamlining the software development lifecycle through proactive code improvement and education.