Your AI builder shipped the Stripe code in an afternoon. Verifying it took a week.

A friend recently built a booking app using a powerful AI tool that generated the entire code, including Stripe Checkout functionality, in just a few hours. The app functioned as intended, allowing users to complete bookings and receive confirmation emails. However, a critical issue emerged when a customer was charged twice for a single booking. The Stripe Checkout feature retries failed to handle this situation properly, resulting in two bookings being created instead of one.

The problem lies in the fact that the code appeared to be well-written and passed initial code reviews without any issues. This highlights a significant gap in the current verification process for AI-generated code. Normal testing methods, such as mocks and staging, are unable to detect this specific bug, as they only verify the happy path and do not account for retries or second deliveries.

To identify the issue, the reporter devised a solution involving a special tool called a "service twin." This tool allows the AI-generated code to be driven intentionally, forcing the Stripe Checkout feature to behave like a real Stripe system, including its retry mechanisms. By using this service twin, the reporter was able to trigger a second delivery and observe the unintended consequence of two bookings being created for a single payment.

The fix for this problem is relatively straightforward: adding a check to verify if a booking event has already been processed before creating a new one. By implementing this simple verification, the reporter was able to prevent the bug from occurring again. This example demonstrates the importance of thorough testing, particularly when it comes to handling edge cases like retries and duplicate deliveries.

The reporter emphasizes the need for better verification methods and tools to catch such issues before they reach production, ensuring the reliability and integrity of AI-built applications.