ansible/docs/docsite/rst/dev_guide/testing_units.rst

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Unit Tests

Unit tests are small isolated tests that target a specific library or module. Unit tests in Ansible are currently the only way of driving tests from python within Ansible's continuous integration process. This means that in some circumstances the tests may be a bit wider than just units.

Topics

Available Tests

Unit tests can be found in test/units. Notice that the directory structure of the tests matches that of lib/ansible/.

Running Tests

The Ansible unit tests can be run across the whole code base by doing:

cd /path/to/ansible/source
source hacking/env-setup
ansible-test units --tox

Against a single file by doing:

ansible-test units --tox apt

Or against a specific Python version by doing:

ansible-test units --tox --python 2.7 apt

For advanced usage see the online help:

ansible-test units --help

You can also run tests in Ansible's continuous integration system by opening a pull request. This will automatically determine which tests to run based on the changes made in your pull request.

Installing dependencies

ansible-test has a number of dependencies. For units tests we suggest using tox.

The dependencies can be installed using the --requirements argument, which will install all the required dependencies needed for unit tests. For example:

ansible-test units --tox --python 2.7 --requirements apache2_module

Note

tox version requirement

When using ansible-test with --tox requires tox >= 2.5.0

The full list of requirements can be found at test/runner/requirements. Requirements files are named after their respective commands. See also the constraints applicable to all commands.

Extending unit tests

Warning

What a unit test isn't

If you start writing a test that requires external services then you may be writing an integration test, rather than a unit test.

Structuring Unit Tests

Ansible drives unit tests through pytest. This means that tests can either be written a simple functions which are included in any file name like test_<something>.py or as classes.

Here is an example of a function:

#this function will be called simply because it is called test_*()

def test_add()
    a = 10
    b = 23
    c = 33
    assert a + b = c

Here is an example of a class:

import unittest

class AddTester(unittest.TestCase)

    def SetUp()
        self.a = 10
        self.b = 23

    # this function will
    def test_add()
      c = 33
      assert self.a + self.b = c

   # this function will
    def test_subtract()
      c = -13
      assert self.a - self.b = c

Both methods work fine in most circumstances; the function-based interface is simpler and quicker and so that's probably where you should start when you are just trying to add a few basic tests for a module. The class-based test allows more tidy set up and tear down of pre-requisites, so if you have many test cases for your module you may want to refactor to use that.

Assertions using the simple assert function inside the tests will give full information on the cause of the failure with a trace-back of functions called during the assertion. This means that plain asserts are recommended over other external assertion libraries.

A number of the unit test suites include functions that are shared between several modules, especially in the networking arena. In these cases a file is created in the same directory, which is then included directly.

Module test case common code

Keep common code as specific as possible within the test/units/ directory structure. For example, if it's specific to testing Amazon modules, it should be in test/units/modules/cloud/amazon/. Don't import common unit test code from directories outside the current or parent directories.

Don't import other unit tests from a unit test. Any common code should be in dedicated files that aren't themselves tests.

Fixtures files

To mock out fetching results from devices, or provide other complex datastructures that come from external libraries, you can use fixtures to read in pre-generated data.

Text files live in test/units/modules/network/PLATFORM/fixtures/

Data is loaded using the load_fixture method

See eos_banner test for a practical example.

If you are simulating APIs you may find that python placebo is useful. See testing_units_modules for more information.

Code Coverage For New or Updated Unit Tests

New code will be missing from the codecov.io coverage reports (see testing), so local reporting is needed. Most ansible-test commands allow you to collect code coverage; this is particularly useful when to indicate where to extend testing.

To collect coverage data add the --coverage argument to your ansible-test command line:

ansible-test units --coverage apt
ansible-test coverage html

Results will be written to test/results/reports/coverage/index.html

Reports can be generated in several different formats:

• ansible-test coverage report - Console report.
• ansible-test coverage html - HTML report.
• ansible-test coverage xml - XML report.

To clear data between test runs, use the ansible-test coverage erase command. See testing_running_locally for more information about generating coverage reports.

testing_units_modules

Special considerations for unit testing modules

testing_running_locally

Running tests locally including gathering and reporting coverage data

Python 3 documentation - 26.4. unittest — Unit testing framework

The documentation of the unittest framework in python 3

Python 2 documentation - 25.3. unittest — Unit testing framework

The documentation of the earliest supported unittest framework - from Python 2.6

pytest: helps you write better programs

The documentation of pytest - the framework actually used to run Ansible unit tests