QuickCheck for Python

## Project description

## Props

Property-based testing for Python à la QuickCheck.

`for_all`

`for_all` takes a list of generators (see below) and a property. It
then tests the property for arbitrary values of the generators.

Here’s an example testing the commutative and associative properties of
`int`s:

for_all(int, int)(lambda a, b: a + b == b + a) for_all(int, int)(lambda a, b: a * b == b * a) for_all(int, int, int)(lambda a, b, c: c * (a + b) == a * c + b * c)

### Generators

*Note:* These are not the same as Python generators. We should rename
them. Generaters? Blech.

A generator is a specification of a set of possible Python objects. A generator is either:

- One of the following built-in types:
`None`,`bool`,`int`,`float`,`long`,`complex`,`str`,`tuple`,`set`,`list`, or`dict`

- A class that implements the ArbitraryInterface
- Or constructed using the generator combinators.

#### Combinators

`maybe_a`- Generates either an arbitrary value of the specified generator or None.

`maybe_an`- An alias for
`maybe_a`. Provided for syntactic convenience.

- An alias for
`one_of`- Generates an arbitrary value of one of the specified generators.

`tuple_of`- Generates a tuple by generating values for each of the specified generators.

`set_of`- Generates a homogeneous set of the specified generator. You can
generate non-homogeneous sets using
`set`.

- Generates a homogeneous set of the specified generator. You can
generate non-homogeneous sets using
`list_of`- Generates a homogeneous list of the specified generator. You can
generate non-homogeneous lists using
`list`.

- Generates a homogeneous list of the specified generator. You can
generate non-homogeneous lists using
`dict_of`- Generates a homogeneous dict of the specified generators using
kwargs. You can generate non-homogeneous dicts using
`dict`.

- Generates a homogeneous dict of the specified generators using
kwargs. You can generate non-homogeneous dicts using

`arbitrary`

`arbitrary` takes a generator and returns a single instance of the
generator.

### ArbitraryInterface

We provide a mixin with one classmethod, `arbitrary`, which raises
`NotImplementedError`. To implement generators for your own classes,
please inherit from ArbitraryInterface and provide an implementation for
`arbitrary`.

Here’s an example implementation of a Binary Tree class:

class BinaryTree(ArbitraryInterface): ... @classmethod def arbitrary(cls): return arbitrary(one_of(Leaf, Node)) class Leaf(BinaryTree): ... @classmethod def arbitrary(cls): return cls(...) # an instance of Leaf. class Node(BinaryTree): ... @classmethod def arbitrary(cls): return cls( ... # This is equivalent: arbitrary(BinaryTree), # to this: BinaryTree.arbitrary() ) # an instance of Node with two subtrees.

#### AbstractTestArbitraryInterface

We also provide an `AbstractTestArbitraryInterface` with you can mixin
to your test cases for each class that implements `ArbitraryInterface`
to ensure the `arbitrary` method is implemented:

class TestBinaryTree(AbstractTestArbitraryInterface, TestCase): def setUp(self): self.obj = BinaryTree

## To Do

- all built in types: http://docs.python.org/2/library/stdtypes.html
- ranges
- import some faker generators for more semantic random values

## Project details

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Filename, size & hash SHA256 hash help | File type | Python version | Upload date |
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props-0.0.2.tar.gz (4.5 kB) Copy SHA256 hash SHA256 | Source | None | Feb 14, 2014 |