HORIZON HASKELLDocslts/ghc-9.10.x248f8f02026-10-05Search names, modules, packages, or :: a typeCtrl K

GHC 9.10.3 · lts/ghc-9.10.x · 248f8f0 · 2026-10-05

Modulehspec-wai-0.11.1Haskell2010

Test.Hspec.Wai.QuickCheck

  • 2 types
  • 2 classes
  • 34 values

Re-exports

34 declarations
classclass Arbitrary a where
#

Random generation and shrinking of values.

QuickCheck provides Arbitrary instances for most types in base, except those which incur extra dependencies. For a wider range of Arbitrary instances see the quickcheck-instances package.

Methods

  • arbitrary :: Gen a

    A generator for values of the given type.

    It is worth spending time thinking about what sort of test data you want - good generators are often the difference between finding bugs and not finding them. You can use sample, label and classify to check the quality of your test data.

    There is no generic arbitrary implementation included because we don't know how to make a high-quality one. If you want one, consider using the testing-feat or generic-random packages.

    The QuickCheck manual goes into detail on how to write good generators. Make sure to look at it, especially if your type is recursive!

  • shrink :: a -> [a]

    Produces a (possibly) empty list of all the possible immediate shrinks of the given value.

    The default implementation returns the empty list, so will not try to shrink the value. If your data type has no special invariants, you can enable shrinking by defining shrink = genericShrink, but by customising the behaviour of shrink you can often get simpler counterexamples.

    Most implementations of shrink should try at least three things:

    1. Shrink a term to any of its immediate subterms. You can use subterms to do this.

    2. Recursively apply shrink to all immediate subterms. You can use recursivelyShrink to do this.

    3. Type-specific shrinkings such as replacing a constructor by a simpler constructor.

    For example, suppose we have the following implementation of binary trees:

    data Tree a = Nil | Branch a (Tree a) (Tree a)

    We can then define shrink as follows:

    shrink Nil = []
shrink (Branch x l r) =
  -- shrink Branch to Nil
  [Nil] ++
  -- shrink to subterms
  [l, r] ++
  -- recursively shrink subterms
  [Branch x' l' r' | (x', l', r') <- shrink (x, l, r)]

    There are a couple of subtleties here:

    • QuickCheck tries the shrinking candidates in the order they appear in the list, so we put more aggressive shrinking steps (such as replacing the whole tree by Nil) before smaller ones (such as recursively shrinking the subtrees).

    • It is tempting to write the last line as [Branch x' l' r' | x' <- shrink x, l' <- shrink l, r' <- shrink r] but this is the wrong thing! It will force QuickCheck to shrink x, l and r in tandem, and shrinking will stop once one of the three is fully shrunk.

    There is a fair bit of boilerplate in the code above. We can avoid it with the help of some generic functions. The function genericShrink tries shrinking a term to all of its subterms and, failing that, recursively shrinks the subterms. Using it, we can define shrink as:

    shrink x = shrinkToNil x ++ genericShrink x
  where
    shrinkToNil Nil = []
    shrinkToNil (Branch _ l r) = [Nil]

    genericShrink is a combination of subterms, which shrinks a term to any of its subterms, and recursivelyShrink, which shrinks all subterms of a term. These may be useful if you need a bit more control over shrinking than genericShrink gives you.

    A final gotcha: we cannot define shrink as simply shrink x = Nil:genericShrink x as this shrinks Nil to Nil, and shrinking will go into an infinite loop.

    If all this leaves you bewildered, you might try shrink = genericShrink to begin with, after deriving Generic for your type. However, if your data type has any special invariants, you will need to check that genericShrink can't break those invariants.

Instances117Arbitrary, …
newtypenewtype Gen a
#

A generator for values of type a.

The third-party packages QuickCheck-GenT and quickcheck-transformer provide monad transformer versions of Gen.

Constructors

  • MkGen
    • unGen :: QCGen -> Int -> a

      Run the generator on a particular seed and size. If you just want to get a random value out, consider using generate.

Instances5Monad, Functor, MonadFix, Applicative, Testable
  • Monad GenDefined in QuickCheck-2.15.0.1 · Test.QuickCheck.Gen
  • Functor GenDefined in QuickCheck-2.15.0.1 · Test.QuickCheck.Gen
  • MonadFix GenDefined in QuickCheck-2.15.0.1 · Test.QuickCheck.Gen
  • Applicative GenDefined in QuickCheck-2.15.0.1 · Test.QuickCheck.Gen
  • Testable prop => Testable (Gen prop)Defined in QuickCheck-2.15.0.1 · Test.QuickCheck.Property
valuechoose :: Random a => (a, a) -> Gen a
#

Generates a random element in the given inclusive range. For integral and enumerated types, the specialised variants of choose below run much quicker.

valuechooseAny :: Random a => Gen a
#

Generates a random element over the natural range of a.

valuefrequency :: HasCallStack => [(Int, Gen a)] -> Gen a
#

Chooses one of the given generators, with a weighted random distribution. The input list must be non-empty.

valuegenerate :: Gen a -> IO a
#

Run a generator. The size passed to the generator is always 30; if you want another size then you should explicitly use resize.

valuegetSize :: Gen Int
#

Returns the size parameter. Used to construct generators that depend on the size parameter.

For example, listOf, which uses the size parameter as an upper bound on length of lists it generates, can be defined like this:

listOf :: Gen a -> Gen [a]
listOf gen = do
  n <- getSize
  k <- choose (0,n)
  vectorOf k gen

You can also do this using sized.

valuegrowingElements :: HasCallStack => [a] -> Gen a
#

Takes a list of elements of increasing size, and chooses among an initial segment of the list. The size of this initial segment increases with the size parameter. The input list must be non-empty.

valuelistOf :: Gen a -> Gen [a]
#

Generates a list of random length. The maximum length depends on the size parameter.

valuelistOf1 :: Gen a -> Gen [a]
#

Generates a non-empty list of random length. The maximum length depends on the size parameter.

valueoneof :: HasCallStack => [Gen a] -> Gen a
#

Randomly uses one of the given generators. The input list must be non-empty.

valueresize :: HasCallStack => Int -> Gen a -> Gen a
#

Overrides the size parameter. Returns a generator which uses the given size instead of the runtime-size parameter.

valuesample :: Show a => Gen a -> IO ()
#

Generates some example values and prints them to stdout.

valuesample' :: Gen a -> IO [a]
#

Generates some example values.

valuescale :: (Int -> Int) -> Gen a -> Gen a
#

Adjust the size parameter, by transforming it with the given function.

valueshuffle :: [a] -> Gen [a]
#

Generates a random permutation of the given list.

valuesized :: (Int -> Gen a) -> Gen a
#

Used to construct generators that depend on the size parameter.

For example, listOf, which uses the size parameter as an upper bound on length of lists it generates, can be defined like this:

listOf :: Gen a -> Gen [a]
listOf gen = sized $ \n ->
  do k <- choose (0,n)
     vectorOf k gen

You can also do this using getSize.

valuesublistOf :: [a] -> Gen [a]
#

Generates a random subsequence of the given list.

valuesuchThat :: Gen a -> (a -> Bool) -> Gen a
#

Generates a value that satisfies a predicate.

valuesuchThatMap :: Gen a -> (a -> Maybe b) -> Gen b
#

Generates a value for which the given function returns a Just, and then applies the function.

valuesuchThatMaybe :: Gen a -> (a -> Bool) -> Gen (Maybe a)
#

Tries to generate a value that satisfies a predicate. If it fails to do so after enough attempts, returns Nothing.

Internals

2 declarations