mock 0.7.0b2

mock is a Python module that provides a core Mock class. It is intended to reduce the need for creating a host of trivial stubs throughout your test suite. After performing an action, you can make assertions about which methods / attributes were used and arguments they were called with. You can also specify return values and set needed attributes in the normal way.

mock is tested on Python versions 2.4-2.7 and Python 3.

The mock module also provides utility functions / objects to assist with testing, particularly monkey patching.

• PDF documentation for 0.7.0b2

• mock on google code (repository and issue tracker)

• mock documentation (0.6.0)

• Article on mocking, patching and stubbing

Mock is very easy to use and is designed for use with unittest. Mock is based on the ‘action -> assertion’ pattern instead of ‘record -> replay’ used by many mocking frameworks. See the mock documentation for full details.

Mock objects create all attributes and methods as you access them and store details of how they have been used. You can configure them, to specify return values or limit what attributes are available, and then make assertions about how they have been used:

>>> from mock import Mock
>>> real = ProductionClass()
>>> real.method = Mock(return_value=3)
>>> real.method(3, 4, 5, key='value')
3
>>> real.method.assert_called_with(3, 4, 5, key='value')

side_effect allows you to perform side effects, return different values or raise an exception when a mock is called:

>>> from mock import Mock
>>> mock = Mock(side_effect=KeyError('foo'))
>>> mock()
Traceback (most recent call last):
 ...
KeyError: 'foo'
>>> values = [1, 2, 3]
>>> def side_effect():
...     return values.pop()
...
>>> mock.side_effect = side_effect
>>> mock(), mock(), mock()
(3, 2, 1)

Mock has many other ways you can configure it and control its behaviour. For example the spec argument configures the mock to take its specification from another object. Attempting to access attributes or methods on the mock that don’t exist on the spec will fail with an AttributeError.

The patch decorator / context manager makes it easy to mock classes or objects in a module under test. The object you specify will be replaced with a mock (or other object) during the test and restored when the test ends:

>>> from mock import patch
 >>> @patch('test_module.ClassName1')
 ... @patch('test_module.ClassName2')
 ... def test(MockClass1, MockClass2):
 ...     test_module.ClassName1()
 ...     test_module.ClassName2()

 ...     assert MockClass1.called
 ...     assert MockClass2.called
 ...
 >>> test()

 >>> with patch.object(ProductionClass, 'method') as mock_method:
 ...     mock_method.return_value = None
 ...     real = ProductionClass()
 ...     real.method(1, 2, 3)
 ...
 >>> mock_method.assert_called_with(1, 2, 3)

There is also patch.dict for setting values in a dictionary just during a scope and restoring the dictionary to its original state when the test ends:

>>> foo = {'key': 'value'}
>>> original = foo.copy()
>>> with patch.dict(foo, {'newkey': 'newvalue'}, clear=True):
...     assert foo == {'newkey': 'newvalue'}
...
>>> assert foo == original

Mock now supports the mocking of Python magic methods. The easiest way of using magic methods is with the MagicMock class. It allows you to do things like:

>>> from mock import MagicMock
>>> mock = MagicMock()
>>> mock.__str__.return_value = 'foobarbaz'
>>> str(mock)
'foobarbaz'
>>> mock.__str__.assert_called_with()

Mock allows you to assign functions (or other Mock instances) to magic methods and they will be called appropriately. The MagicMock class is just a Mock variant that has all of the magic methods pre-created for you (well - all the useful ones anyway).

The following is an example of using magic methods with the ordinary Mock class:

>>> from mock import Mock
>>> mock = Mock()
>>> mock.__str__ = Mock()
>>> mock.__str__.return_value = 'wheeeeee'
>>> str(mock)
'wheeeeee'

mocksignature is a useful companion to Mock and patch. It creates copies of functions that delegate to a mock, but have the same signature as the original function. This ensures that your mocks will fail in the same way as your production code if they are called incorrectly:

>>> from mock import mocksignature
>>> def function(a, b, c):
...     pass
...
>>> function2 = mocksignature(function)
>>> function2.mock.return_value = 'fishy'
>>> function2(1, 2, 3)
'fishy'
>>> function2.mock.assert_called_with(1, 2, 3)
>>> function2('wrong arguments')
Traceback (most recent call last):
 ...
TypeError: <lambda>() takes exactly 3 arguments (1 given)

The distribution contains tests and documentation. The tests require unittest2 to run.