Models are worse at reviewing their own code

Rodrigo from Greptile explores the efficacy of AI models, specifically those from OpenAI and Anthropic, in reviewing their own code versus code authored by other models. By analyzing datasets of pull requests (PRs) authored by Claude Code and Codex, he finds that models are more successful at identifying bugs in code written by other models rather than their own. This phenomenon is partly due to each model's tendency to miss bugs similar to those it introduces. Claude models tend to cast a wider net, providing numerous comments in reviews, while GPT models focus more deeply, often leaving some bugs unreported due to a strong emphasis on verification. To address these discrepancies and enhance bug detection, Greptile introduces a "Model Inversion" feature, where code authored by one model is reviewed by a different model, capitalizing on their complementary strengths. The study highlights the intricacies of model behavior and the ongoing challenge of balancing alignment with practical utility in AI code review systems.

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