Exam is a Python toolkit for writing better tests. It aims to remove a lot of the boiler plate testing code one often writes, while still following Python conventions and adhering to the unit testing interface.
A simple pip install exam
should do the trick.
Rationale --------
Aside from the obvious "does the code work?", writings tests has many additional goals and bennefits:
- If written semantically, reading tests can help demostrate how the code is supposed to work to other developers.
- If quick running, tests provide feedback during development that your changes are working or not having an adverse side effects.
- If they're easy to write correctly, developers will write more tests and they will be of a higher quality.
Unfortunately, the common pattern for writing Python unit tests tends to not offer any of these advantages. Often times results in ineffecient and unnessarily obtuse testing code. Additionally, common uses of the mock library can often result in repetitive boiler-plate code or ineffeciency during test runs.
exam aims to improve the state of Python test writing by providing a toolkit of useful functionality to make writing quick, correct and useful tests and painless as possible.
Exam features a collection of useful modules:
Exam has some useful decorators to make your tests easier to write and understand. To utilize the @before
, @after
, @around
and @patcher
decorators, you must mixin the exam.cases.Exam
class into your test case. It implements the appropriate setUp()
and tearDown()
methods necessary to make the decorators work.
Note that the @fixture
decorator works without needing to be defined inside of an Exam class. Still, it's a best practice to add the Exam
mixin to your test cases.
All of the decorators in exam.decorators
, as well as the Exam
test case are available for import from the main exam
package as well. I.e.:
from exam import Exam
from exam import fixture, before, after, around, patcher
The @fixture
decorator turns a method into a property (similar to the @property
decorator, but also memoizes the return value. This lets you reference the property in your tests, i.e. self.grounds
, and it will always reference the exact same instance every time.
from exam.decorators import fixture
from exam.cases import Exam
class MyTest(Exam, TestCase):
@fixture
def user(self):
return User(name='jeff')
def test_user_name_is_jeff(self):
assert self.user.name == 'jeff'
As you can see, self.user
was used to reference the user
property defined above.
If all your fixture method is doing is contructing a new instance of type or calling a class method, exam provides a shorthand inline fixture
syntax for constructing fixture objects. Simply set a class variable equal to fixture(type_or_class_method)
and exam witll automatically call your type or class method.
from exam.decorators import fixture
from exam.cases import Exam
class MyTest(Exam, TestCase):
user = fixture(User, name='jeff')
def test_user_name_is_jeff(self):
assert self.user.name == 'jeff'
Any *args
or **kwargs
passed to fixture(type_or_class_method)
will be passed to the type_or_class_method
when called.
The @before
decorator adds the method to the list of methods which are run as part of the class's setUp()
routine.
from exam.decorators import before
from exam.cases import Exam
class MyTest(Exam, TestCase):
@before
def reset_database(self):
mydb.reset()
@before
also hooks works through subclasses - that is to say, if a parent class has a @before
hook in it, and you subclass it and define a 2nd @before
hook in it, both @before
hooks will be called. Exam runs the child class's @before
hook first, then runs the parents'.
The compliment to @before
, @after
adds the method to the list of methods which are run as part of the class's tearDown()
routine. Like @before
, @after
runs child class @after
hooks before running their parents'
from exam.decorators import after
from exam.cases import Exam
class MyTest(Exam, TestCase):
@after
def remove_temp_files(self):
myapp.remove_temp_files()
The @patcher
decorator is shorthand for the following boiler plate code:
from mock import patch
def setUp(self):
self.stats_patcher = patch('mylib.stats', new=dummy_stats)
self.stats = self.stats_patcher.start()
def tearDown(self):
self.stats_patcher.stop()
Often, manually controlling a patch's start/stop is done to provide a test case property (here, self.stats
) for the mock object you are patching with. This is handy if you want the mock to have defaut behavior for most tests, but change it slightly for certain ones -- i.e absorb all calls most of the time, but for certain tests have it raise an exception.
Using the @patcher
decorator, the above code can simply be written as:
from exam.decorators import patcher
from exam.cases import Exam
class MyTest(Exam, TestCase):
@patcher('mylib.stats')
def stats(self):
return dummy_stats
Exam takes care of starting and stopping the patcher appropriately, as well as constructing the patch
object with the return value from the decorated method.
If you're happy with the default constructed mock object for a patch (MagicMock
), then patcher
can simply be used as an inline as a function inside the class body. This method still starts and stops the patcher when needed, and returns the constructed MagicMock
object, which you can set as a class attribute. Exam will add the MagicMock
object to the test case as an instance attribute automatically.
from exam.decorators import patcher
from exam.cases import Exam
class MyTest(Exam, TestCase):
logger = patcher('coffee.logger')
The helpers
module features a collection of helper methods for common testing patterns:
The track
helper is intended to assist in tracking call orders of independent mock objects. track
is called with kwargs, where the key is the mock name (a string) and the value is the mock object you want to track. track
returns a newly constructed MagicMock
object, with each mock object attached at a attribute named after the mock name.
For example, below track()
creates a new mock with tracker.cool` as the
cool_mockand
tracker.heatas the
heat_mock. .. code:: python from exam.helpers import track @mock.patch('coffee.roast.heat') @mock.patch('coffee.roast.cool') def test_roasting_heats_then_cools_beans(self, cool_mock, heat_mock): tracker = track(heat=heat_mock, cool=cool_mock) roast.perform() tracker.assert_has_calls([mock.call.heat(), mock.call.cool()])
exam.helpers.rm_f^^^^^^^^^^^^^^^^^^^^^ This is a simple helper that just removes all folders and files at a path: .. code:: python from exam.helpers import rm_f rm_f('/folder/i/do/not/care/about')
exam.helpers.mock_import^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Removes most of the boiler plate code needed to mock imports, which usually consists of making a
patch.dictfrom
sys.modules. Instead, the
patch_importhelper can simply be used as a decorator or context manager for when certain modules are imported. .. code:: python from exam.helpers import mock_import with mock_import('os.path') as my_os_path: import os.path as imported_os_path assert my_os_path is imported_os_path
mock_importcan also be used as a decorator, which passed the mock value to the testing method (like a normal
@patch) decorator: .. code:: python from exam.helpers import mock_import @mock_import('os.path') def test_method(self): import os.path as imported_os_path assert my_os_path is imported_os_path
exam.mock~~~~~~~~~~~~~ Exam has a subclass of the normal
mock.Mockobject that adds a few more useful methods to your mock objects. Use it in place of a normal
Mockobject: .. code:: python from exam.mock import Mock mock_user = Mock(spec=User) The subclass has the following extra methods: *
assert_called()- Asserts the mock was called at least once. *
assert_not_called()- Asserts the mock has never been called. *
assert_not_called_with(args,kwargs)`` - Asserts the mock was not most recently called with the specified ``argsand
kwargs``. * ``assert_not_called_once_with(*args,kwargs)- Asserts the mock has only every been called once with the specified
args`` and ``kwargs``. assert_not_any_call(*args, **kwargs)
- Asserts the mock has never been called with the specified *args
and **kwargs
.
Helpful fixtures that you may want to use in your tests:
exam.fixtures.two_px_square_image
- Image data as a string of a 2px square image.exam.fixtures.one_px_spacer
- Image data as a string of a 1px square spacer image.
Useful objectgs for use in testing:
exam.objects.noop
- callable object that always returns None
. no matter how it was called.
Exam is MIT licensed. Please see the LICENSE
file for details.