This research introduces a hybrid approach that integrates Large Language Models with traditional static code analyzers to improve automated code review capabilities.

• Combines the precision of rule-based static analyzers with the contextual understanding of LLMs
• Creates more accurate, relevant, and comprehensive code reviews than either approach alone
• Demonstrates improved performance in detecting both simple code issues and complex context-dependent problems
• Provides more actionable feedback with specific recommendations for code improvement

For engineering teams, this hybrid approach offers significant potential to streamline development workflows, improve code quality, and reduce the time developers spend on manual reviews while maintaining high standards.

Original Paper: Combining Large Language Models with Static Analyzers for Code Review Generation