Skip to content

HTTPS clone URL

Subversion checkout URL

You can clone with HTTPS or Subversion.

Download ZIP
powerful mock, expectation and unit test helpers for python
Python Makefile
tag: 0.3.0

Fetching latest commit…

Cannot retrieve the latest commit at this time

Failed to load latest commit information.
mocktest
test
.gitignore
Capfile
LICENCE
README.markdown
setup.py

README.markdown

mocktest (version 0.3)

... is a powerful and easy-to-use mocking library, inspired by rspec and similar in many ways to Michael Foord's popular Mock module.

The main features are:

  • powerful expectation matching behaviour for mock objects
  • automatic verification of expectations (before each test's tearDown)
  • automatic rollback of inserted mock objects after each test

It's released under the BSD licence (see the LICENCE file).

Source / Readme: http://github.com/gfxmonk/mocktest/tree/master

Issues / discussion: http://code.google.com/p/python-mocktest/

Cheese shop entry: http://pypi.python.org/pypi/mocktest

Important notes for this version

In mocktest 0.3, a few key objects and attributes have been renamed:

  • the mock_wrapper() function is now just called mock()
  • to get a raw mock from a mock wrapper, you now use .raw instead of .mock

To sum up, where you would previously use mock_wrapper().mock you now would use mock().raw. The new names are more concise and less confusing to new users.

Where did it come from?

I am a big fan of rspec, but less of a fan of ruby as a whole. I wanted a to use rspec's powerful should_receive() and associated matchers with my python mock objects.

mocktest is by no means a port of rspec - it is smaller and simpler, and a lot more pythonic.

what are mocks?

What are mocks used for? mocks can pretend to be pretty much any object. Mocks record what happens to them (accessors, method calls, etc) and allow you to verify that this is what you expected. Replace a database connection with a mock and make sure the right commands are being sent to your database - without the overhead and trouble of using an actual database. Replace os.system with a mock, and supply your own response to shell commands. Mocks can be used to satisfy dependencies or simulate external conditions in your program so you can focus on unit-level testing.


mocktest

TestCase

When using mocktest, you should always use mocktest.TestCase instead of unittest.TestCase. Mocktest's version is almost identical, but automatically calls the required setup and teardown hooks for the mocktest library.

There is one important addition to the mocktest.TestCase class:

  • assertRaises(exception_type, callable, < ... >)

Where additional args can be:

  • args=(arg1,arg2) ** Fails unless the arguments provided to the exception constructor match the given args
  • message="some string" ** Fails unless the error message (i.e. str(exception)) is equal to this message
  • matches="string" or matches=re.compile("match_string", custom_flags) ** Just like message, except a regex.search is required to return a match instead of requiring the strings to be identical

This was adapted from http://code.activestate.com/recipes/307970/


mocks

mocktest is still a young framework, and is likely to evolve. While the inspiration is from rspec, a lot of the mechanics differ either necessarily because of differences between ruby and python, and a bunch of things were done differently to make things cleaner.

mocks in mocktest have 3 components. This might sound like two more than you would need, but bear with me:

  • raw mock: a raw mock is a minimal mock object that can be put wherever you need it. It records interaction that happens to it, but it can't distinguish between interaction as part of your test and interaction caused by the rest of the program.

    Some mock frameworks only have this object, but adding methods for controlling and inspecting a mock interferes with the actual mock behaviour itself. For this reason, mocktest has a puppet-master of sorts:

  • mock wrapper: a mock wrapper is how you talk to a raw mock behind-the-scenes. It allows you to set the return value for a method, or see how many times and with what arguments a raw mock has been called. In your unit test source code, you will mostly be interacting with mock wrappers to define behaviour and specify your expectations.

  • mock anchor: finally, there is one piece missing in the puzzle. You have a mock, and you talk to it through a mock wrapper. But how do you insert it where it needs to go? A mock anchor latches on to a root object, and records every mock you create on it. Creating a mock called "some_method" on a mock anchor will attach it to parent.some_method. And more importantly, when your unit test is done it will revert parent.some_method to what it was before.

    By using a mock anchor, you ensure that your mocks never live past the lifespan of your test case - this can cause havok in other mock frameworks.

So, how do you use all of these things?

anchor = mock_on(some_object)

creates a mock anchor attached to some_object.

wrapper = anchor.foo

creates a raw mock called foo, and attaches it to some_object.foo. The value of this expression is a mock wrapper linked to the newly-created some_object.foo raw mock.

If your mock is not attached to anything, you can create a standalone mock wrapper:

wrapper = mock()

you can get the raw mock (to provide to your code) with:

raw_mock = wrapper.raw

and if you just have a raw mock object, you can get a wrapper for it by calling:

wrapper = mock(raw_mock)

This might seem a little confusing, but hopefully the examples below will help.


Mock customisation

A mock has a few options for specifying its behaviour:

wrapper = mock()
wrapper.name = "my mock"
wrapper.return_value = "result!"

which will result in:

>>> raw_mock = wrapper.raw
>>> str(raw_mock)
'my mock'

>>> raw_mock()
'result!'

The other property your mock wrapper has is:

def my_action(*args):
    print "called with: %r" % (args,)
    return "result..."
wrapper.action = my_action

resulting in:

>>> wrapper.raw('a','b','c')
called with: ['a','b','c']
'result...'

note: You cannot use both action and return_value on the same mock, you'll get a MockError. If you want to have an action and particular return value, return the value from the action.

Because setting properties like this is a little verbose, mock wrapper objects provide some helpful methods. These methods all return the mock wrapper itself, so you can chain them together.

mock().named('my mock')
mock().returning(10)
mock().with_action(lambda x: x + 1)

In addition, there are some additional methods which don't directly relate to attributes:

mock().raising(IOError)
mock().raising(IOError('permission denied'))

raising takes an exception class or instance and raises it when the mock is called. This overwrites the mock's action attribute (and will fail if you have already set an action).

By default, calling raw_mock.some_attribute will force some_attribute to be added to the mock. If you don't want this behaviour, you can lock down the mock using:

mock().frozen()

This will raise an AttributeError when any new attribute is accessed (or set) on the mock.

mock().with_children('x', 'y', z='zed')
mock().with_methods('x','y', z='zed')
mock().with_spec(some_object)

Children and methods are similar. They take in any number of string arguments and keyword arguments. String arguments ensure that an attribute of that name exists on the mock. Keyword arguments specify its value, as well.

The difference between methods and children is that the value of a method is used for a child's return_value:

>>> wrapper = mock().with_methods('y', z='zed')
>>> wrapper.raw.z()
'zed'

whereas child values are used as-is:

>>> wrapper = mock().with_children('y', z='zed')
>>> wrapper.raw.z
'zed'

If you have an object that you want to mimic, you can use:

mock().with_spec(some_object)

If some_object has attributes "foo" and "bar", so too will your mock. The values for these attributes are mocks; they do not copy the spec_object's attribute values.

Calling with_methods, with_children or with_spec has the side effect of freezing the mock. Any attributes that aren't already on the mock cannot be added. If you want to control this yourself, use wrapper.frozen() and wrapper.unfrozen()

If you want to, you can also override special methods on a mock:

>>> wrapper = mock().with_children( __len__ = lambda x: 5 )
>>> len(wrapper.raw)
5

There's a bit of black magic to special method overriding, so please send bug reports if you find something that doesn't work.


Expectations

Having earlier said that mocktest is not rspec, here are a bunch of useful examples ported from the rspec documentation

The basic setup of a test case is identical to using unittest.TestCase:

from mocktest import *

    class MyTestClass(TestCase):
        def setUp(self):
            # common setup actions...

        def tearDown(self):
            # common teardown actions...

        def test_feature_a(self):
            #test the functionality in feature a

        def test_feature_b(self):
            #test the functionality in feature b

Expecting calls

mock_os = mock_on(os)
mock_os.system.is_expected

This will fail your test unless os.system() is called at least once during the current test case (the check is made right before the tearDown() method is executed)

If you don't want an anchored mock, you can use:

wrapper = mock()
raw_mock = wrapper.raw
wrapper.is_expected

You can then pass raw_mock into a function and ensure that it is called. But you should not set os.system = raw_mock. This will change os.system for the life of your tests, and will almost certainly mess up the rest of your test cases. That is why the mock_on() function exists to automatically clean up your mocks.

Multiplicites of calls

The default is_expected ensures that your method is called at least once. There are other options:

mock_anchor.method.is_expected.no_times() # shouldn't be called
mock_anchor.method.is_expected.once() # once (and no more)
mock_anchor.method.is_expected.twice()
mock_anchor.method.is_expected.thrice() # (isn't thrice a great word?)

mock_anchor.method.is_expected.exactly(4).times
mock_anchor.method.is_expected.at_least(10).times
mock_anchor.method.is_expected.at_most(2).times

this also works just fine:

mock_anchor.method.is_expected.at_most(2)

("times" is unnecessary, but it helps for readability)

Expecting Arguments

mock_anchor.method.is_expected.with(<args>)

e.g:

mock_anchor.method.is_expected.with_args(1, 2, 3)
mock_anchor.method.is_expected.with_args(1, 2, 3, foo='bar').once()
mock_anchor.method.is_expected.with_args() # No arguments allowed

Note: When adding conditions to a call, the multiplicity (number of calls) is checked after the other conditions. This means that while the following will fail:

mock_anchor.method.is_expected.once()
myobj.action('a')
myobj.action('b')

this will succeed:

mock_anchor.method.is_expected.once().with('a')
myobj.action('a')
myobj.action('b')

This is the same way that rspec works, and it is the most flexible, since you can always put a bound on the total invocations by adding a non-conditional multiplicity check:

mock_anchor.method.is_expected.twice()

(you can apply as many is_expected's to a single mock as you like)

Argument Constraints

When you don't know the exact arguments, you can supply a checking function. If this function does not return True, the expectation fails:

mock_anchor.method.is_expected.where_args(lambda arg1, arg2: arg1 == arg2)
mock_anchor.method.is_expected.where_args(lambda arg: isinstance(arg, dict))

It doesn't have to be an inline lambda expression:

def check_args(*args, **kwargs):
    if len(args) > 3:
        return False
    if 'bad_argument' in kwargs:
        return False
    return True

mock_anchor.method.is_expected.where_args(check_args)

Proxying

Proxying options are confusingly similar to argument constraints. But where argument constraints validate the arguments that the mock is called with, proxying controls weather the mock intercepts a method call in the first place. An example:

class MyObject(object):
    def do_something(self, some_number):
        return some_number + 10
obj = MyObject()

# (note the use of `obj` to match the implicit `self` paramater)
wrapper = mock_on(obj).do_something.with_args(obj, 5).returning(500)
mock_do_something = wrapper.raw

# now if the arguments we give to the mock don't match what we supplied to
# with_args, the call will go ahead just as if we hadn't set a mock on obj:

assert mock_do_something(1) == 11
assert mock_do_something(2) == 12

# but if the arguments do match:
assert mock_do_something(5) == 500

# note that only the intercepted call is recorded
assert wrapper.called.once()

Just like argument constraints, you can also use where_args - e.g:

def second_arg_is_a_string(a, b):
    return isinstance(b, str)

wrapper = mock_on(obj).do_something.where_args(second_arg_is_a_string).returning("STRING")
mock_do_something = wrapper.raw

assert mock_do_something(1) == 11
assert mock_do_something(2) == 12

# but if the arguments do match:
assert mock_do_something('5') == "STRING"

Post-checking

Specifying your expectations before anything happens is sometimes not the best (or easiest) thing to do.

It's possible to just inspect the state of a mock to see what's happened to it so far. called is almost identical to is_expected. Unlike an expectation object, The result of a called expression should be compared to True or False to check whether the expressed call(s) did indeed happen.

self.assertTrue(mock().called.once().with_args('foo'))
if not mock().called.once():
    assert False, "Things went bad!"

But the most useful feature of of called is its ability to retrieve the calls that the mock has received. So in the following example:

wrapper = mock()
raw_mock = wrapper.raw

raw_mock('a', b='foo')
raw_mock('b')
raw_mock(b='bar')

>>> wrapper.called.thrice().get_calls()
[(('a',), {'b': 'foo'}), ('b',), (None, {'b': 'bar'})]

Note that where a call has no arguments or has no keyword-arguments, the first or second element (respectively) in the call tuple is None instead of an empty tuple or dict. This is mostly for readability, because there are already enough parentheses in the mix.

Note: get_calls will fail if the assertions made after called are not met. e.g: if mock has been called once and you ask for wrapper.called.twice().get_calls(), then you'll get an AssertionError.

If you're only expecting one call, you can use get_args:

mock(b='bar')
>>> wrapper.called.once().get_args()

Note that get_args requires you to explicitly specify once().


Testing the mocktest library

I use nosetests, and just run it from the root directory. You probably should too!

Thanks

Michael Foord

Something went wrong with that request. Please try again.