Summary

Large Language Models (LLMs) have revolutionized numerous areas of software engineering, and this paper introduces a new LLM-based approach to improve code review processes. By training on large code repositories, including reviews and bug reports, the AI model aims to predict potential risks and suggest improvements, enhancing coding efficiency and developer education.

Key Insights

• The LLM-based AI agent improves code review by predicting future risks, providing suggestions based on past data.
• Empirical studies suggest its effectiveness in reducing post-release bugs and enhancing manual code review processes.
• Future work will assess the accuracy and efficiency of LLM-generated documentation, with a focus on empirical studies comparing manual methods.

Further Research

This research provides a foundation for further exploration into the integration of LLMs in software development, particularly in improving automated code quality assessments and developer training.