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deepmind.google·6d ago
AI Engineering
Lobsters·17d ago·5 min read
Reviewing AI Code Is Not A Viable Argument
TL;DR
LLM Coding Assistants are criticized for their high error rates, which undermine their utility in software development. The author argues that proponents fail to address empirical evidence of these shortcomings.
✦ Why It Matters
Engineers should critically evaluate the reliability of LLM Coding Assistants before integrating them into their workflows.
Key Takeaways
- Assess the reliability of LLM-generated code before use.
- Limit code reviews to 400 lines and one hour for effectiveness.
- Conduct empirical studies on reviewing LLM outputs.
- Be cautious of claims regarding productivity improvements from LLMs.
Full Summary
The article critiques the use of Large Language Model (LLM) Coding Assistants in software development, arguing that their tendency to produce incorrect code resembles the unreliability of interns. It highlights the need for thorough code reviews, which are limited by empirical research showing that effective reviews should not exceed 400 lines of code and should last no longer than one hour.
Given these constraints, a developer can only review a few thousand lines of code per day, which diminishes the expected productivity gains from using LLMs. Furthermore, the author points out that human reviewers may be less effective at identifying defects in LLM-generated code compared to human-generated code.
The call for more empirical research on the review process of LLM outputs is emphasized, as current evidence suggests that these tools may not deliver the promised benefits in code quality or efficiency.