<ul><li>AI coding tools like GitHub Copilot and ChatGPT, while offering faster development and fewer repetitive tasks, often lead to subtle bugs and security vulnerabilities that make it to production.</li><li>Studies show that around 32% of AI-generated code is incorrect, leading to a 41% increase in bugs and 30% of code containing security vulnerabilities post-adoption of AI tooling.</li><li>Traditional testing methods such as unit tests, code reviews, and integration tests failed to keep up with the rapid code generation by AI, leading to more bugs slipping through and increasing technical debt.</li><li>Discovering Model-Based Testing (MBT) as a smarter way to test AI-generated code, a developer found Provengo, a platform that utilizes MBT, helped catch AI errors before production, automated testing maintenance, and integrated smoothly into their CI/CD pipeline, reducing debugging time by around 60% and increasing confidence in AI-generated code.</li></ul>

🚀Fixing AI Code with Model-Based Testing: A Developer's Tale

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