Our new report: AI code creates 1.7x more problems

AI coding assistants have become integral to development workflows, significantly increasing the speed of code production, but also raising concerns about quality due to a higher incidence of errors. Analysis by CodeRabbit of 470 open-source GitHub pull requests, including both AI-assisted and human-only contributions, revealed that AI-generated pull requests contained approximately 1.7 times more issues overall, with notable increases in logic, readability, security, and formatting errors. These problems are not unique to AI but are more prevalent, suggesting that while AI can enhance output, it also amplifies certain mistakes. The study underscores the importance of providing AI with contextual information, enforcing style and security protocols, and adopting robust code review practices to mitigate the risks associated with AI-generated code. The findings highlight that while AI tools can accelerate development, they require deliberate engineering and safety measures to ensure code quality, suggesting a need for AI-aware practices and tools such as CodeRabbit to standardize reviews and maintain high standards in production environments.