Testing

p.. _testing:

Testing

Every proposed change to a charm is run through testing during the review verification process. If you want to contribute a change or fix to a charm, please take time to review the Unit Testing and Functional Testing sections of this document.

OpenStack Charm CI will verify your changes, but please execute at least unit tests locally before submitting patches to reduce load on the OpenStack CI infrastructure.

The OpenStack Charms are compliant with the OpenStack Consistent Testing Interface; take a read on how this works to understand in full.

Lint

You can verify the compliance of your code changes using flake8 and the charm proof tool using the pep8 tox environment:

tox -e pep8

Ensure that any non-compliance is corrected prior to raising/updating a review.

Unit Testing

Execute the synthetic code checks (unit tests) for a charm using the tox environment specific for the version of Python you wish to test.

tox -e py36

Note

The environment name specified in the above example is a moving target, and you may need to adapt it depending on what is the current version of Python used in the test gate.

Unit tests are stored in the unit_tests folder; when adding features or changing existing code, please ensure that appropriate unit tests are added or updated to cover the changes you are making.

Unit tests are written in Python using standard mocking techniques to isolate the unit tests from the underlying host operating system.

Writing Unit Tests

Writing software, and functions, is an art. It’s a balance of conciseness, simplicity, ergonomics and readability. To produce software that only meets the feature needs, whilst being maintainable and correct for the life of the program.

Writing unit tests is, similarly, an art. The objective is to write tests that correctly determine that what a function does is correct, rather than how the function achieves it - with the exception, in some cases, of performance. But that’s another whole ball-game.

The goal is, for each function under test, is to verify that the outputs of the function are correct for comprehensive sets of inputs to the function. What is not the objective is to test the internal implementation of the function.

It’s worth exploring what are the inputs and outputs of a function, and that depends on whether the function is pure or impure.

A pure function is one that is always returns the same results for the same set of values passed to the function. This means that there are no (input) side-effects or dependencies on any other state outside of the function. A pure function is analogous, algorithimically, to a mathematical function. Pure functions also can only call other pure functions. i.e. a pure function isn’t pure if it calls a function that is impure. Impurity at a particular level ‘infects’ every caller of that function.

An impure function is basically a function that is not a pure function. i.e. it may depend on a global variable, or obtain inputs from side-effects (such as reading IO functions). It is also impure if it has any output side-effects.

So, in addition to the parameters passed to a function, other inputs (within a function) are accessing functions that access global state. e.g. the config() function, relation functions, and reading from files, or the network.

Also, in addition to the return value of a function, other outputs (within the function) are writing to files, using the network, calling subprocess calls, and other IO operations.

So the goal with unit-testing a function depends on the purity of the function:

  • Pure functions require no mocking. The object is to verify that for combinations of input parameter values, that the correct return values are presented. As pure functions are pure ‘all the way down’, no mocking is required, as they will always be consistent for any set of inputs. Pure functions are also fantastic opportunities to use property-based testing. However, most of the work in a charm is all about side-effects, so most functions are impure.

  • Impure functions require mocking. The goal is to isolate the function-under-test from it’s side-effects so that only the function is being checked. If the function calls other impure functions, they should be mocked out. Tests for a function should only test that function, and not other functions as a by-product.

Strategies for writing Unit Tests

It’s important to test what a function does, not how the function does it. This is basically a re-statement of the idea to not test the implementation of a function, but rather the output (and with respect to impure functions) the side-effects.

If the unit test is dependent on the implementation of the function (not with respect to side-effects), then that locks in the implementation, which makes refactoring much harder. i.e. To refactor a function that does the same thing, the unit test would also have to change. This is the sign of a poor unit test.

Individual unit tests should be small and test one activation of the function-under-test. This way, behaviour changes during refactoring, or adding features, will break the smallest number of tests, and show what behaviour has changed quickly. Complex unit tests are more fragile and tend to therefore come with a higher maintenance burden.

With impure functions, it’s important to mock out the side-effects so that the test doesn’t also test other side-effect functions.

Functional Testing

Note

This section is out of date, we are moving to the Zaza framework as part of our effort to modernize the test suite and as a consequence of the Python 2 language nearing is end of life.

Amulet

Functional tests for a charm are written using the Amulet test framework and should exercise the target charm with a subset of a full OpenStack deployment to ensure that the charm is able to correctly deploy and configure the service that is encapsulates.

The OpenStack charm helpers provide some Amulet deployment helpers to ease testing of different OpenStack release combinations; typically each charm will test the OpenStack and Ubuntu release combinations currently supported by Ubuntu.

The OpenStack Charms Amulet tests in their current form may be specific to execution within a tenant on an OpenStack cloud, via the Juju OpenStack provider, and that is how the third-party-CI executes them. Future functional test enhancements include the ability run the tests against the Juju OpenStack provider (a cloud) or the Juju LXD provider (all on one machine).

Full Amulet

Executes all Amulet gate tests (may take several hours). The full Amulet test set does not run automatically on each proposed change. After the lower-cost lint, unit, charm-single and Amulet-smoke tests have completed, reviewers can conduct code reviews then optionally trigger the full set of Amulet tests (see Rechecking).

To manually trigger execution of all Amulet tests on your locally-defined cloud:

tox -e func27
Amulet Smoke

Executes a subset (generaly one) of the Amulet deployment test sets. The Amulet smoke test set does run automatically on every proposed patchset.

To manually trigger execution of the Amulet smoke test on your locally-defined cloud:

tox -e func27-smoke
No-Op

Builds a Python virtualenv per definitions in tox.ini, which can be useful in test authoring.

To manually trigger a build of the virtualenv on your local machine, but execute no tests:

tox -e func27-noop

Test methods are called in lexical sort order, as with most test runners. However, each individual test method should be idempotent and expected to pass regardless of run order or Ubuntu:OpenStack combo. When writing or modifying tests, ensure that every individual test is not dependent on another test method.

Some tests may need to download files from the Internet, such as glance images. If a web proxy server is required in the environment, the AMULET_HTTP_PROXY environment variable must be set. This is unrelated to Juju’s http-proxy settings.

See tox.ini to determine specifically which test targets will be executed by each tox target. Amulet tests reside in the tests/ directory for classic charms, and in the src/tests/ directory for layered source charms.

Rechecking

BEFORE issuing a recheck of any kind, please inspect the CI results and log artifacts to understand the failure reason.

Rechecks should only be used in the event of a system failure (not for race conditions or problems introduced by the proposed code changes).

Developers are expected to have executed tests prior to submitting patches.

Tests can be retriggered, or additional tests can be requested, simply by replying on the Gerrit review with one of the recognized magic phrases below.

recheck

Re-triggers events as if a new patchset had been submitted, including all defined OpenStack Infra tests AND third-party-CI tests.

charm-recheck

Re-triggers only the default set of OpenStack Charms third-party-ci tests, but not the OpenStack Infra tests. Depending on system load and which charm is under test, this will typically take 30 to 60 minutes.

charm-recheck-full

Triggers a full set of OpenStack Charms third-party-ci tests, but not the OpenStack Infra tests. This will take several hours.

Creative Commons Attribution 3.0 License

Except where otherwise noted, this document is licensed under Creative Commons Attribution 3.0 License. See all OpenStack Legal Documents.