Injecting static analyzer output into LLM prompts (RAG) improves code review accuracy and coverage over the LLM alone, while data-augmented training improves coverage only.
Core: Resolving code quality issues using llms,
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Combining Large Language Models with Static Analyzers for Code Review Generation
Injecting static analyzer output into LLM prompts (RAG) improves code review accuracy and coverage over the LLM alone, while data-augmented training improves coverage only.