Author: | Michael Foord |
---|---|
Co-maintainer: | Konrad Delong |
Version: | Mock 0.7.0 beta 1 |
Date: | 2010/06/XX |
Homepage: | Mock Homepage |
Download: | Mock on PyPI |
Documentation: | PDF Documentation |
License: | BSD License |
Support: | Testing in Python Email List |
Issue tracker: | Google code project |
mock provides a core Mock class that is intended to reduce the need to create 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 specific attributes in the normal way.
The mock module also provides a patch() decorator that handles patching module and class level attributes within the scope of a test, along with sentinel for creating unique objects. See the quick guide for some examples of how to use Mock, MagicMock and patch.
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.
mock is tested on Python versions 2.4-2.7 and Python 3.
The current version is 0.7.0 beta 1, dated XXXX. Mock is still experimental; the API may change. If you find bugs or have suggestions for improvements / extensions then please email me.
You can checkout the latest development version from the Google Code Subversion repository with the following command:
svn checkout http://mock.googlecode.com/svn/trunk/ mock-read-only
If you have pip, setuptools or distribute you can install mock with:
easy_install mockpip install mock
Alternatively you can download the mock distribution from PyPI and after unpacking run:
python setup.py install
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 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)
Articles and blog entries on testing with Mock:
Mock uses unittest2 for its own test suite. In order to run it, use the unit2 script that comes with unittest2 module on a checkout of the source repository:
unit2 discover
On Python 2.7 and 3.2 you can use unittest module from the standard library.
python3.2 -m unittest discover
On Python 2.4 you will see one failure - testwith.py will fail to import as it uses the with statement which is invalid syntax under Python 2.4.
On Python 3 the tests for unicode are skipped as they are not relevant.