𝗬𝗼𝘂𝗿 𝗔𝗜-𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗲𝗱 𝗰𝗼𝗱𝗲 𝗰𝗮𝗻 𝗽𝗮𝘀𝘀 𝗲𝘃𝗲𝗿𝘆 𝘁𝗲𝘀𝘁 𝗮𝗻𝗱 𝘀𝘁𝗶𝗹𝗹 𝗯𝗲 𝘄𝗿𝗼𝗻𝗴.
Imagine a discount function that passes all its tests but accepts a 150% discount and returns a negative total.
The tests are green. The implementation is wrong.
How does this happen?
Because the implementation and its tests can share the same incorrect assumption.
This is why 𝗽𝗮𝘀𝘀𝗶𝗻𝗴 𝘁𝗲𝘀𝘁𝘀 𝗶𝘀 𝗻𝗼𝘁 𝘁𝗵𝗲 𝘀𝗮𝗺𝗲 𝗮𝘀 𝗽𝗿𝗼𝘃𝗶𝗻𝗴 𝗰𝗼𝗿𝗿𝗲𝗰𝘁𝗻𝗲𝘀𝘀.
In my latest article, I explore:
- Why happy-path tests can create false confidence
- How contract and boundary testing expose hidden bugs
- Where property-based testing and mutation testing help
- What developers should verify when working with AI-generated code
I’ve also published a reproducible Python experiment so you can run the tests and see the gap yourself.
📖 𝗥𝗲𝗮𝗱 𝘁𝗵𝗲 𝗮𝗿𝘁𝗶𝗰𝗹𝗲:
https://medium.com/@pranavdixit20/ai-generated-code-can-pass-every-test-and-still-be-wrong-7ddf3177b721
💻 𝗘𝘅𝗽𝗹𝗼𝗿𝗲 𝘁𝗵𝗲 𝗰𝗼𝗱𝗲:
https://github.com/PranavDixit20/ai-code-test-gap
𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻 𝗳𝗼𝗿 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿𝘀: When reviewing AI-generated code, what do you trust most—passing tests, code review, or independently defined requirements?
