EReru·Jul 18, 2024
I have to admit the last time I seriously worked with QuickCheck was quite a while ago.
> My understanding is that Haskell’s QuickCheck can shrink functions [...]
Are you talking about shrinking a value of type (a -> b) (for some concrete a and b)? In Hypothesis, if you write a generator that spits out functions, you get a shrinker for free. So that shrinker will shrink functions.
As far as I can tell, QuickCheck's shrinking is based on values. I just checked, the type is still `shrink :: a -> [a]`, so that can't have changed.
In Hypothesis shrinking is based on the generation process. In Hypothesis generators act a bit like 'Functors' in Haskell, ie you can map over them. (You can also filter and `<>` and do monadic binds etc.)
So in Hypothesis you can take a generator that produces integers, and create one that produces only even n
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umbers by (in Haskell terms) mapping (2) over it. Now, the shrinker you get for free will also only produce even numbers.
That's not possible (or at least not easily possible) in Haskell. You would have to define at least a newtype wrapper.
Have a look at https://hypothesis.works/articles/compositional-shrinking/ and https://hypothesis.works/articles/integrated-shrinking/ for some more write-up, especially about how Hypothesis can preserve information even when shrinking past a monadic bind.
If you follow some links in the two articles above, you land at https://github.com/icicle-lang/disorder.hs-ambiata/tree/mast... which is a Haskell library that shares a few design decisions with Hypothesis.
Apart from shrinking, Haskell's QuickCheck can also learn a lot from Hypothesis in terms of floating point number generation. The last time I used QuickCheck seriously, they basically generated floats by converting from a sort-of uniformly sampled fractional number. I see that this commit https://github.com/nick8325/quickcheck/commit/07942642d7987b... seems to have made float generation a lot smarter!
Hypothesis is especially 'nasty' when it generates floats. It has a good chunk of probability allocated to things like various NaNs, infinities, sub-normal numbers, zero, one float past zero, etc, plus of course some probability mass on uniformly chosen floats. See https://hypothesis.readthedocs.io/en/latest/data.html#hypoth... for the docs, but you should check out the code, too.