The practical and philosophical problems with AI code review

The exploration of AI-assisted code review, particularly using GPT-4, has demonstrated both the potential and limitations of integrating large language models (LLMs) into software development workflows. While GPT-4 can quickly generate reviews and highlight issues like spelling mistakes and minor logical errors, its accuracy is hindered by false positives and a lack of comprehensive codebase context. Efforts to improve AI review involved techniques such as introducing an "AI review guide" to align AI reviews with team preferences and using retrieval-augmented-generation (RAG) to provide better context. Despite these enhancements, the AI reviewer's signal-to-noise ratio remains insufficient, and philosophical concerns about aspects like author trust, reviewer learning, and accountability persist. Although AI may eventually serve as a supplementary tool by acting as a super-linter or providing contextual insights, human oversight and final approval are likely to remain crucial to ensure code quality and security.

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