AI-Powered Automated Code Review for GitHub: A Survey of Static Analysis, Large Language Models, Hybrid Approaches, and Developer Workflows

Abstract: Automated code review has become an important research direction in software engineering because conventional manual review is time-consuming and dependent on reviewer expertise. Traditional static analysis tools can efficiently identify predefined coding violations and security weaknesses, but they have limited understanding of program intent and may produce false-positive warnings. Recent advances in Large Language Models (LLMs) provide new capabilities for code understanding, defect detection, explanation generation, and automated review. This survey examines recent research on AI-powered automated code review, covering static analysis, machine learning, LLM-based approaches, hybrid analysis, automated program repair, vulnerability detection, and GitHub-based workflows. Twenty research papers are comparatively analyzed with respect to methodology, tools, datasets, evaluation metrics, advantages, limitations, and applicability to automated GitHub code review. The survey identifies important research gaps, including false positives, hallucination, limited contextual understanding, computational cost, language-specific analysis, and limited integration with developer workflows. Based on these findings, the survey motivates a hybrid architecture that combines deterministic static analysis with context-aware LLM reasoning and GitHub Pull Request integration.

Keywords: Automated Code Review, Large Language Models, Static Analysis, GitHub, Software Engineering, Code Intelligence, Code Security