And OK it's not equivalent to a formal proof, but passing 1,000+ tests that cover every aspect of the specification is pretty close from a practical perspective, especially for a visual formatting tool.
https://www2.eecs.berkeley.edu/Pubs/TechRpts/2025/EECS-2025-...
So I think under some computer science theory case for arbitrary functions its not possible, but for the actual shape of behavior in question from this library I think its realistic that a decent corpus of 'real' examples and then differential fuzzing would give you more confidence that anyone has in nearly any program's correctness here on real Earth.
When I hear guarantee, it makes me think of correctness proofs.
Confidence is more of a practical notion for how much you trust the system for a given use case. Testing can definitely provide confidence in this scenario.
There are only 8 32-bit Mersenne primes, 4 of which are byte-valued. Fuzzing might catch the bug, if it happened to hit one of the four other 32-bit Mersenne primes (which, in many fuzzers, is more likely than a uniform distribution would suggest), but I'm sure you can imagine situations where it wouldn't.
Sure you would. If the mutation tester mutates that lookup table. Which is quite easy to do, and which mutmut will do (if that lookup table is inside a function, because mutmut is based on mutant schemata).
Or branch coverage for the lesser version, the idea is still to generate interesting cases based on each implementation, not based solely on one of them.