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This article analyzes the quality, security, and maintainability of code generated by leading AI models like GPT-5.2 High and Gemini 3 Pro using SonarQube. It presents findings on functional performance, complexity, concurrency issues, and security vulnerabilities across various models.
This article analyzes a report comparing AI-generated and human-written code, focusing on the higher incidence of issues in AI pull requests. Key findings show that AI code often has more critical errors, readability problems, and security vulnerabilities, highlighting the need for better review processes.