expectise 2.1.0

Mocking API and function calls in Python - inspired by Ruby's RSpec-Mocks.

Expectise

Mocking function and method calls in Python - inspired by Ruby's RSpec-Mocks.

Description

Test environments are usually isolated from external services, and meant to check the execution logic of the code exclusively. However it is common for projects to deal with external APIs (to receive or post data for example) or systems (such as databases), which are not accessible in a test environment.

This package offers a simple, natural way to mock external calls in order to test their surrounding logic, and increase the coverage of tests as much as possible.

Contents

This repo contains:

• the expectise module itself, under /expectise;
• a dummy example of small module and its tests under /example with unit tests showcasing what the expectise package can do. Each test case is generously commented to explain how the expectise package is used.

Install

Install from Pypi:

pip install expectise

Running Tests with Expectise

Mocking functions and methods follows 3 steps:

1. Markers: marking a function or method as mocked. Once marked, the function or method is replaced by a placeholder function, and its calls intercepted.
2. Expectations: describing the expected behavior of the mocked function or method inside the unit tests.
3. Teardown: resetting the mocking behaviour so that all unit tests are fully independent and don't interfere with each other. During that step, some infractions can be caught too, such as not having called a method that was supposed to be called.

1/ Markers

Functions and methods can be marked as mocked in 2 different ways, that are described below.

Permanent Markers

Using the mock_if decorator, along with the name and value of the environment variable you use to identify your test environment. This environment variable, say ENV, will be checked at interpretation time: if its value matches the input, say ENV=test, the mocking logic will be implemented; if not, nothing in your code will be modified, and performance will stay the same since nothing will happen passed interpretation.

Example of decoration:

from expectise import mock_if


class MyObject:
    ...

    @mock_if("ENV", "test")
    def my_function(self, ...)
        ...

    ...

This approach is concise, explicit and transparent: you can identify mocked methods at a glance, and your tests can remain light without any setup logic. However, it means patching production code, and carrying a dependency on this package in your production environment, which may be seen as a deal breaker from an isolation of concerns perspective.

Temporary Markers

Using explicit mock statements when setting up your tests. Before running individual tests, mocks can be injected explicitly, typically through fixtures if you're familiar with pytest (you'll find examples in examples/tests/).

Example of statement:

import pytest
from expectise import mock


@pytest.fixture(autouse=True)
def run_around_tests():
    mock(SomeObject.some_method)
    yield
    # see next section for more details on tear down actions

This approach is a little bit heavier, and may require more maintenance when mocked objects are modified. However, it keeps a clear separation of concerns, with production code that is not altered and does not have to depend on this package.

2/ Expectations

The following use cases are covered:

• asserting that a function or method is called (the right number of times),
• checking the arguments passed to a function or method,
• overriding a function or method so that it returns a given output when called,
• overriding a function or method so that it raises an exception when called.

The above features can be combined too, with the following 4 possible patterns:

Expect(MyObject.my_method).to_return(my_object)
Expect(MyObject.my_method).to_raise(my_error)
Expect(MyObject.my_method).to_receive(*my_args, **my_kwargs).and_return(my_object)
Expect(MyObject.my_method).to_receive(*my_args, **my_kwargs).and_raise(my_error)

A given function or class method can be decorated several times, with different arguments to check and ouputs to be returned. You just have to specify it with several Expect statements. In this case, the order of the statements matters.

The following is valid and assumes my_method is going to be called three times exactly:

Expect(MyObject.my_method).to_receive(*my_args_1, **my_kwargs_1).and_return(my_object_1)
Expect(MyObject.my_method).to_receive(*my_args_2, **my_kwargs_2).and_raise(my_error)
Expect(MyObject.my_method).to_receive(*my_args_3, **my_kwargs_3).and_return(my_object_2)

Note that if a function or class method decorated at least once with an Expect statement is called more or less times than the number of Expect statements, the unit test will fail. You may also face a situation where disabling a mock is useful - for example, to write a test for a function or method decorated with mock_if. To achieve this, simply call disable_mock(my_callable).

3/ Tear Down

Once a test has run, underlying expectise objects have to be reset so that 1) some final checks can happen, and 2) new tests can be run with no undesirable side effects from previous tests. There are 2 ways of performing the necessary tear down actions, described below.

1. Using the Expectations context manager

We recommend using this approach if only a few of your tests deal with functions or class methods that you want to mock. Toy example:

from expectise import Expect
from some_module.some_api import SomeAPI


def test_instance_method():
    with Expectations():
        Expect(SomeAPI).to_receive("update_attribute").and_return("sshhhh")
        ...
        assert SomeAPI().update_attribute("secret_value") == "sshhhh"

2. Calling tear_down explicitly

We recommend using this approach if most of your tests manipulate objects that you want to mock. Reusing the pytest fixtures example from the previous section:

import pytest
from expectise import mock
from expectise import tear_down


@pytest.fixture(autouse=True)
def run_around_tests():
    mock(SomeObject.some_method)
    yield
    tear_down()

Contributing

Local Setup

We recommend using asdf for managing high level dependencies. With asdf installed,

1. simply run asdf install at the root of the repository,
2. run poetry install to install python dependencies.

Running Tests

eval $(poetry env activate)
ENV=test python -m pytest -v example/tests/