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.
Unit tests can be found in test/units. Notice that the directory
structure of the tests matches that of
To run unit tests using docker, always use the default docker image
by passing the
--docker default argument.
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 --docker -v
Against a single file by doing:
ansible-test units --docker -v apt
Or against a specific Python version by doing:
ansible-test units --docker -v --python 2.7 apt
If you are running unit tests against things other than modules, such as module utilities, specify the whole file path:
ansible-test units --docker -v test/units/module_utils/basic/test_imports.py
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.
If you are running
ansible-test with the
--venv option you do not need to install dependencies manually.
Otherwise you can install dependencies using the
--requirements option, which will
install all the required dependencies needed for unit tests. For example:
ansible-test units --python 2.7 --requirements apache2_module
The list of unit test requirements can be found at test/units/requirements.txt.
This does not include the list of unit test requirements for
which can be found at test/lib/ansible_test/_data/requirements/units.txt.
See also the constraints applicable to all test commands.
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.
Ansible drives unit tests through pytest. This
means that tests can either be written a simple functions which are included in any file
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
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.
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.
To mock out fetching results from devices, or provide other complex data structures that
come from external libraries, you can use
fixtures to read in pre-generated data.
Text files live in
Data is loaded using the
See eos_banner test for a practical example.
If you are simulating APIs you may find that python placebo is useful. See Unit Testing Ansible Modules for more information.
New code will be missing from the codecov.io coverage reports (see Testing Ansible), 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
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 Ansible for more information about generating coverage
- Unit Testing Ansible Modules
Special considerations for unit testing modules
- Testing Ansible
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