๐ฌ๐ผ๐๐ฟ ๐๐-๐ด๐ฒ๐ป๐ฒ๐ฟ๐ฎ๐๐ฒ๐ฑ ๐ฐ๐ผ๐ฑ๐ฒ ๐ฐ๐ฎ๐ป ๐ฝ๐ฎ๐๐ ๐ฒ๐๐ฒ๐ฟ๐ ๐๐ฒ๐๐ ๐ฎ๐ป๐ฑ ๐๐๐ถ๐น๐น ๐ฏ๐ฒ ๐๐ฟ๐ผ๐ป๐ด.
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 ๐ฝ๐ฎ๐๐๐ถ๐ป๐ด ๐๐ฒ๐๐๐ ๐ถ๐ ๐ป๐ผ๐ ๐๐ต๐ฒ ๐๐ฎ๐บ๐ฒ ๐ฎ๐ ๐ฝ๐ฟ๐ผ๐๐ถ๐ป๐ด ๐ฐ๐ผ๐ฟ๐ฟ๐ฒ๐ฐ๐๐ป๐ฒ๐๐.
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
Iโve also published a reproducible Python experiment so you can run the tests and see the gap yourself.
๐ ๐ฅ๐ฒ๐ฎ๐ฑ ๐๐ต๐ฒ ๐ฎ๐ฟ๐๐ถ๐ฐ๐น๐ฒ:
https://medium.com/@pranavdixit20/ai-generated-code-can-pass-every-test-and-still-be-wrong-7ddf3177b721
๐ป ๐๐
๐ฝ๐น๐ผ๐ฟ๐ฒ ๐๐ต๐ฒ ๐ฐ๐ผ๐ฑ๐ฒ:
https://github.com/PranavDixit20/ai-code-test-gap
๐ค๐๐ฒ๐๐๐ถ๐ผ๐ป ๐ณ๐ผ๐ฟ ๐ฑ๐ฒ๐๐ฒ๐น๐ผ๐ฝ๐ฒ๐ฟ๐: When reviewing AI-generated code, what do you trust mostโpassing tests, code review, or independently defined requirements?
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