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 presents a leaderboard ranking various LLMs based on their performance in code quality, security, and maintainability. The analysis evaluates 4,444 Java programming assignments, providing metrics like pass rates and issue density for each model. Key insights include the top-performing models and their specific strengths.

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.