An AI-Powered Automated Code Review System Using Large Language Models and Static Analysis

Abstract: Code review is a critical phase in the software development lifecycle, ensuring code quality, maintainability, and security. However, manual code reviews are labor-intensive, often requiring 2 to 4 hours per pull request, and are highly susceptible to human error. Junior developers may inadvertently miss critical security vulnerabilities. This paper proposes an advanced AI-powered automated code review system that seamlessly integrates traditional rule-based static analysis with the deep contextual reasoning capabilities of Large Language Models (LLMs). Operating as an event-driven microservice, the system automatically triggers upon the creation of a GitHub Pull Request, passing Python code changes through a tri-layered analysis engine: Bandit for security vulnerability detection, Pylint for coding standard enforcement, and the Groq LLM for complex logical review and contextual feedback. Results are aggregated, mathematically ranked by severity, and posted directly to the pull request as a structured comment within a 60-second execution window. Deployed on a cost-effective stack including Render, Neon PostgreSQL, and FastAPI, this framework reduces review bottlenecks, minimizes security flaws, and enhances developer productivity.

Keywords: Automated Code Review, Large Language Models, Static Analysis, Continuous Integration, Software Security, Artificial Intelligence, GitHub Actions.