Effective Code Review Practices for Vibe Coding

Determine which code review practices—specifically run-and-see checks, automated tests, and AI-assisted reviews—are effective when applied to AI-generated code produced via vibe coding, defined as building software primarily by describing goals in natural language and iteratively prompting AI code generation tools while relying on minimal review of the generated code.

Background

The paper defines vibe coding as producing software primarily through natural-language prompting of AI code generation tools, with minimal review of the generated code. Across the grey literature sources analyzed, the authors find frequent skipping of QA, uncritical trust in AI outputs, and delegation of QA back to the AI, which raises concerns about reliability and maintainability.

Given this widespread departure from traditional verification practices, identifying which code review strategies actually work in the rapid, intuition-driven context of vibe coding is essential. The authors explicitly note uncertainty around the effectiveness of different review methods and highlight the need for research that can inform practical, built-in QA workflows tailored to vibe coding’s fast prototyping pace.