Maintainer | Roman Cheplyaka <roma@ro-che.info> |
---|---|

Safe Haskell | Safe-Infered |

Generation of test data.

- type Depth = Int
- type Series a = Depth -> [a]
- class Serial a where
- cons0 :: a -> Series a
- cons1 :: Serial a => (a -> b) -> Series b
- cons2 :: (Serial a, Serial b) => (a -> b -> c) -> Series c
- cons3 :: (Serial a, Serial b, Serial c) => (a -> b -> c -> d) -> Series d
- cons4 :: (Serial a, Serial b, Serial c, Serial d) => (a -> b -> c -> d -> e) -> Series e
- alts0 :: Series a -> Series a
- alts1 :: Serial a => Series b -> Series (a -> b)
- alts2 :: (Serial a, Serial b) => Series c -> Series (a -> b -> c)
- alts3 :: (Serial a, Serial b, Serial c) => Series d -> Series (a -> b -> c -> d)
- alts4 :: (Serial a, Serial b, Serial c, Serial d) => Series e -> Series (a -> b -> c -> d -> e)
- (\/) :: Series a -> Series a -> Series a
- (><) :: Series a -> Series b -> Series (a, b)
- newtype N a = N a
- type Nat = N Int
- type Natural = N Integer
- depth :: Depth -> Depth -> Depth

# Basic definitions

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.

coseries :: Series b -> Series (a -> b)Source

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.

Serial Bool | |

Serial Char | |

Serial Double | |

Serial Float | |

Serial Int | |

Serial Integer | |

Serial () | |

Serial a => Serial [a] | |

Serial a => Serial (Maybe a) | |

(Integral a, Serial a) => Serial (N a) | |

(Serial a, Serial b) => Serial (a -> b) | |

(Serial a, Serial b) => Serial (Either a b) | |

(Serial a, Serial b) => Serial (a, b) | |

(Serial a, Serial b, Serial c) => Serial (a, b, c) | |

(Serial a, Serial b, Serial c, Serial d) => Serial (a, b, c, d) |

# Data Generators

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

Writing SmallCheck generators 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 a => Serial (Tree a) where series = cons0 Null \/ cons3 Fork

The default interpretation of depth for datatypes is the depth of nested
construction: constructor functions, including those for newtypes, build
results with depth one greater than their deepest argument. But this
default can be over-ridden by composing a `consN`

application with an
application of `depth`

, like this:

newtype Light a = Light a instance Serial a => Serial (Light a) where series = cons1 Light . depth 0

The depth of `Light x`

is just the depth of `x`

.

## What does consN do, exactly?

`consN`

has type
`(Serial t_1, ..., Serial t_N) => (t_1 -> ... -> t_N -> t) -> Series t`

.

`consN f`

is a series which, for a given depth `d > 0`

, produces values of the
form

f x_1 ... x_N

where `x_i`

ranges over all values of type `t_i`

of depth up to `d-1`

(as defined by the `series`

functions for `t_i`

).

If `d <= 0`

, no values are produced.

# Function Generators

To generate functions of an application-specific argument type
requires a second method `coseries`

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

coseries rs d = [ \t -> case t of Null -> z Fork t1 x t2 -> f t1 x t2 | z <- alts0 rs d , f <- alts3 rs d ] coseries rs d = [ \l -> case l of Light x -> f x | f <- (alts1 rs . depth 0) d ]

## What does altsN do, exactly?

`altsN`

has type
`(Serial t_1, ..., Serial t_N) => Series t -> Series (t_1 -> ... -> t_N -> t)`

.

`altsN s`

is a series which, for a given depth `d`

, produces functions of
type

t_1 -> ... -> t_N -> t

If `d <= 0`

, these are constant functions, one for each value of `s 0`

.

If `d > 0`

, these functions inspect each of their arguments up to depth
`d-1`

(as defined by the `coseries`

functions for the corresponding
types) and return values given by `s d`

.

alts4 :: (Serial a, Serial b, Serial c, Serial d) => Series e -> Series (a -> b -> c -> d -> e)Source

# Automated Derivation of Generators

For small examples, Series instances are easy enough to define by hand, following the above patterns. But for programs with many or large data type definitions, automatic derivation using a tool such as "derive" is a better option. For example, the following command-line appends to Prog.hs the Series instances for all data types defined there.

$ derive Prog.hs -d Serial --append

## Using GHC Generics

For GHC users starting from GHC 7.2.1 there's also an option to use GHC's
Generics to get `Serial`

instance for free.

Example:

{-# LANGUAGE DeriveGeneric #-} import Test.SmallCheck import GHC.Generics data Tree a = Null | Fork (Tree a) a (Tree a) deriving Generic instance Serial a => Serial (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.