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

Modulesmallcheck-1.2.1.1Haskell2010

Test.SmallCheck.Series

You need this module if you want to generate test values of your own types.

You'll typically need the following extensions:

{-# LANGUAGE FlexibleInstances, MultiParamTypeClasses #-}

SmallCheck itself defines data generators for all the data types used by the Prelude.

In order to generate values and functions of your own types, you need to make them instances of Serial (for values) and CoSerial (for functions). There are two main ways to do so: using Generics or writing the instances by hand.

  • 6 types
  • 2 classes
  • 32 values

Generic instances

0 declarations

The easiest way to create the necessary instances is to use GHC generics (available starting with GHC 7.2.1).

Here's a complete example:

{-# LANGUAGE FlexibleInstances, MultiParamTypeClasses #-}
{-# LANGUAGE DeriveGeneric #-}

import Test.SmallCheck.Series
import GHC.Generics

data Tree a = Null | Fork (Tree a) a (Tree a)
    deriving Generic

instance Serial m a => Serial m (Tree a)

Here we enable the DeriveGeneric extension which allows to derive Generic instance for our data type. Then we declare that Tree a is an instance of Serial, but do not provide any definitions. This causes GHC to use the default definitions that use the Generic instance.

One minor limitation of generic instances is that there's currently no way to distinguish newtypes and datatypes. Thus, newtype constructors will also count as one level of depth.

Data Generators

0 declarations

Writing Serial instances for application-specific types is straightforward. You need to define a series generator, typically using consN family of generic combinators where N is constructor arity.

For example:

data Tree a = Null | Fork (Tree a) a (Tree a)

instance Serial m a => Serial m (Tree a) where
  series = cons0 Null \/ cons3 Fork

For newtypes use newtypeCons instead of cons1. The difference is that cons1 is counts as one level of depth, while newtypeCons doesn't affect the depth.

newtype Light a = Light a

instance Serial m a => Serial m (Light a) where
  series = newtypeCons Light

For data types with more than 6 fields define consN as

consN f = decDepth $
  f <$> series
    <~> series
    <~> series
    <~> ...    {- series repeated N times in total -}

What does consN do, exactly?

consN has type (Serial t₁, ..., Serial tₙ) => (t₁ -> ... -> tₙ -> t) -> Series t.

consN f is a series which, for a given depth d > 0, produces values of the form

f x₁ ... xₙ

where xₖ ranges over all values of type tₖ of depth up to d-1 (as defined by the series functions for tₖ).

consN functions also ensure that xₖ are enumerated in the breadth-first order. Thus, combinations of smaller depth come first (assuming the same is true for tₖ).

If d \le 0, no values are produced.

Function Generators

0 declarations

To generate functions of an application-specific argument type, make the type an instance of CoSerial.

Again there is a standard pattern, this time using the altsN combinators where again N is constructor arity. Here are Tree and Light instances:

instance CoSerial m a => CoSerial m (Tree a) where
  coseries rs =
    alts0 rs >>- \z ->
    alts3 rs >>- \f ->
    return $ \t ->
      case t of
        Null -> z
        Fork t1 x t2 -> f t1 x t2
instance CoSerial m a => CoSerial m (Light a) where
  coseries rs =
    newtypeAlts rs >>- \f ->
    return $ \l ->
      case l of
        Light x -> f x

For data types with more than 6 fields define altsN as

altsN rs = do
  rs <- fixDepth rs
  decDepthChecked
    (constM $ constM $ ... $ constM rs)
    (coseries $ coseries $ ... $ coseries rs)
    {- constM and coseries are repeated N times each -}

What does altsN do, exactly?

altsN has type (Serial t₁, ..., Serial tₙ) => Series t -> Series (t₁ -> ... -> tₙ -> t).

altsN s is a series which, for a given depth d , produces functions of type

t₁ -> ... -> tₙ -> t

If d \le 0 , these are constant functions, one for each value produced by s.

If d > 0 , these functions inspect each of their arguments up to the depth d-1 (as defined by the coseries functions for the corresponding types) and return values produced by s. The depth to which the values are enumerated does not depend on the depth of inspection.

Basic definitions

4 declarations
typetype Depth = Int
#

Maximum depth of generated test values.

For data values, it is the depth of nested constructor applications.

For functional values, it is both the depth of nested case analysis and the depth of results.

newtypenewtype Series (m :: Type -> Type) a
#

Series is a MonadLogic action that enumerates values of a certain type, up to some depth.

The depth bound is tracked in the Series monad and can be extracted using getDepth and changed using localDepth.

To manipulate series at the lowest level you can use its Monad, MonadPlus and MonadLogic instances. This module provides some higher-level combinators which simplify creating series.

A proper Series should be monotonic with respect to the depth — i.e. localDepth (+1) s should emit all the values that s emits (and possibly some more).

It is also desirable that values of smaller depth come before the values of greater depth.

Instances7MonadTrans, Monad, Functor, Applicative, Alternative, MonadPlus, …
  • MonadTrans SeriesDefined in smallcheck-1.2.1.1 · Test.SmallCheck.SeriesMonad
  • Monad (Series m)Defined in smallcheck-1.2.1.1 · Test.SmallCheck.SeriesMonad
  • Functor (Series m)Defined in smallcheck-1.2.1.1 · Test.SmallCheck.SeriesMonad
  • Applicative (Series m)Defined in smallcheck-1.2.1.1 · Test.SmallCheck.SeriesMonad
  • Alternative (Series m)Defined in smallcheck-1.2.1.1 · Test.SmallCheck.SeriesMonad
  • MonadPlus (Series m)Defined in smallcheck-1.2.1.1 · Test.SmallCheck.SeriesMonad
  • Monad m => MonadLogic (Series m)Defined in smallcheck-1.2.1.1 · Test.SmallCheck.SeriesMonad
classclass Monad m => Serial (m :: Type -> Type) a where
#

Methods

Instances64Serial, …
classclass Monad m => CoSerial (m :: Type -> Type) a where
#

Methods

  • coseries :: Series m b -> Series m (a -> b)

    A proper coseries implementation should pass the depth unchanged to its first argument. Doing otherwise will make enumeration of curried functions non-uniform in their arguments.

Instances60CoSerial, …

Generic implementations

2 declarations

Convenient wrappers

4 declarations
newtypenewtype Positive a
#

Positive x guarantees that x > 0 .

Constructors

Instances12Functor, Foldable, Traversable, Serial, Bounded, Enum, …
newtypenewtype NonNegative a
#

NonNegative x guarantees that x \ge 0 .

Constructors

Instances12Functor, Foldable, Traversable, Serial, Bounded, Enum, …
newtypenewtype NonZero a
#

NonZero x guarantees that x \ne 0 .

Constructors

Instances12Functor, Foldable, Traversable, Serial, Bounded, Enum, …

Other useful definitions

15 declarations
method(>>-) :: m a -> (a -> m b) -> m b
#

Fair conjunction. Similarly to the previous function, consider the distributivity law, naturally expected from MonadPlus:

(a <|> b) >>= k = (a >>= k) <|> (b >>= k)

If a >>= k can backtrack arbitrarily many times, b >>= k may never be considered. In logic statements, "backtracking" is the process of discarding the current possible solution value and returning to a previous decision point where a new value can be obtained and tried. For example:

Example1 expression
do { x <- pure 0 <|> pure 1 <|> pure 2; if even x then pure x else empty } :: [Int][0,2]

Here, the x value can be produced three times, where <|> represents the decision points of that production. The subsequent if statement specifies empty (fail) if x is odd, causing it to be discarded and a return to an <|> decision point to get the next x.

The statement "a >>= k can backtrack arbitrarily many times" means that the computation is resulting in empty and that a has an infinite number of <|> applications to return to. This is called a conjunctive computation because the logic for a and k must both succeed (i.e. pure a value instead of empty).

Similar to the way interleave allows both branches of a disjunctive computation, the >>- operator takes care to consider both branches of a conjunctive computation.

Consider the operation:

odds = pure 1 <|> fmap (2 +) odds

oddsPlus n = odds >>= \a -> pure (a + n)

g = do x <- (pure 0 <|> pure 1) >>= oddsPlus
       if even x then pure x else empty
Example1 expression
observeMany 3 g...never completes...

This will never produce any value because all values produced by the do program come from the pure 1 driven operation (adding one to the sequence of odd values, resulting in the even values that are allowed by the test in the second line), but the pure 0 input to oddsPlus generates an infinite number of empty failures so the even values generated by the pure 1 alternative are never seen. Using interleave here instead of <|> does not help due to the aforementioned distributivity law.

Also note that the do notation desugars to >>= bind operations, so the following would also fail:

do a <- pure 0 <|> pure 1
   x <- oddsPlus a
   if even x then pure x else empty

The solution is to use the >>- in place of the normal monadic bind operation >>= when fairness between alternative productions is needed in a conjunction of statements (rules):

h = do x <- (pure 0 <|> pure 1) >>- oddsPlus
       if even x then pure x else empty
Example1 expression
observeMany 3 h[2,4,6]

However, a bit of care is needed when using >>- because, unlike >>=, it is not associative. For example:

Example7 expressions
let m = [2,7] :: [Int]let k x = [x, x + 1]let h x = [x, x * 2]m >>= (\x -> k x >>= h)[2,4,3,6,7,14,8,16](m >>= k) >>= h -- same as above[2,4,3,6,7,14,8,16]m >>- (\x -> k x >>- h)[2,7,3,8,4,14,6,16](m >>- k) >>- h -- central elements are different[2,7,4,3,14,8,6,16]

This means that the following will be productive:

(pure 0 <|> pure 1) >>-
  oddsPlus >>-
    \x -> if even x then pure x else empty

Which is equivalent to

((pure 0 <|> pure 1) >>- oddsPlus) >>-
  (\x -> if even x then pure x else empty)

But the following will not be productive:

(pure 0 <|> pure 1) >>-
  (\a -> (oddsPlus a >>- \x -> if even x then pure x else empty))

Since do notation desugaring results in the latter, the RebindableSyntax or QualifiedDo language pragmas cannot easily be used either. Instead, it is recommended to carefully use explicit >>- only when needed.

Here is an action of (>>-) on lists:

Example1 expression
take 20 $ [100,200..500] >>- (\x -> map (x +) [1..])[101,201,102,301,103,202,104,401,105,203,106,302,107,204,108,501,109,205,110,303]

The result is map (100 +) [1..] interleaved with [200,300..500] >>- (x -> map (x +) [1..]). You can see that a half of the numbers starts from 1, a quarter starts from 2, and so on exponentially. One could argue that (>>-) is a very unfair conjunction!

valuegenerate :: (Depth -> [a]) -> Series m a
#

A simple series specified by a function from depth to the list of values up to that depth.

valuelistSeries :: Serial Identity a => Depth -> [a]
#

Given a depth, return the list of values generated by a Serial instance.

For example, list all integers up to depth 1:

  • listSeries 1 :: [Int]   -- returns [0,1,-1]
valuefixDepth :: Series m a -> Series m (Series m a)
#

Fix the depth of a series at the current level. The resulting series will no longer depend on the "ambient" depth.

valuedecDepthChecked :: Series m a -> Series m a -> Series m a
#

If the current depth is 0, evaluate the first argument. Otherwise, evaluate the second argument with decremented depth.

Orphan instances

1 instance