Backend Django tests¶
Zulip uses the Django framework for its Python back end. We use the testing framework from django.test to test our code. We have over a thousand automated tests that verify that our backend works as expected.
All changes to the Zulip backend code should be supported by tests. We enforce our testing culture during code review, and we also use coverage tools to measure how well we test our code. We mostly use tests to prevent regressions in our code, but the tests can have ancillary benefits such as documenting interfaces and influencing the design of our software.
If you have worked on other Django projects that use unit testing, you will probably find familiar patterns in Zulip’s code. This document describes how to write tests for the Zulip back end, with a particular emphasis on areas where we have either wrapped Django’s test framework or just done things that are kind of unique in Zulip.
Our tests live in
zerver/tests/. You can run them with
./tools/test-backend. It generally takes about a minute to run
the entire test suite. When you are in iterative mode, you
can run individual tests or individual modules, following the
dotted.test.name convention below:
cd /srv/zulip ./tools/test-backend zerver.tests.tests.WorkerTest
There are many command line options for running Zulip tests, such
--verbose option. The
best way to learn the options is to use the online help:
We also have ways to instrument our tests for finding code coverage,
URL coverage, and slow tests. Use the
-h option to discover these
features. We also have a
--profile option to facilitate profiling
Another thing to note is that our tests generally “fail fast,” i.e. they
stop at the first sign of trouble. This is generally a good thing for
iterative development, but you can override this behavior with the
How to write tests.¶
Before you write your first tests of Zulip, it is worthwhile to read
the rest of this document, and you can also read some of the existing tests
zerver/tests to get a feel for the patterns we use.
A good practice is to get a “failing test” before you start to implement your feature. First, it is a useful exercise to understand what needs to happen in your tests before you write the code, as it can help drive out simple design or help you make incremental progress on a large feature. Second, you want to avoid introducing tests that give false positives. Ensuring that a test fails before you implement the feature ensures that if somebody accidentally regresses the feature in the future, the test will catch the regression.
Setting up data for tests¶
All tests start with the same fixture data. (The tests themselves
update the database, but they do so inside a transaction that gets
rolled back after each of the tests complete. For more details on how the
fixture data gets set up, refer to
The fixture data includes a few users that are named after Shakesepeare characters, and they are part of the “zulip.com” realm.
Generally, you will also do some explicit data setup of your own. Here are a couple useful methods in ZulipTestCase:
More typically, you will use methods directly from the backend code. (This ensures more end-to-end testing, and avoids false positives from tests that might not consider ancillary parts of data setup that could influence tests results.)
Here are some example action methods that tests may use for data setup:
Zulip Testing Philosophy¶
If there is one word to describe Zulip’s philosophy for writing tests, it is probably “flexible.” (Hopefully “thorough” goes without saying.)
When in doubt, unless speed concerns are prohibitive, you usually want your tests to be somewhat end-to-end, particularly for testing endpoints.
These are some of the testing strategies that you will see in the Zulip test suite...
We strive to test all of our URL endpoints. The vast majority of Zulip endpoints support a JSON interface. Regardless of the interface, an endpoint test generally follows this pattern:
- Set up the data.
- Login with
self.login()or set up an API key.
- Use a Zulip test helper to hit the endpoint.
- Assert that the result was either a success or failure.
- Check the data that comes back from the endpoint.
Generally, if you are doing endpoint tests, you will want to create a
test class that is a subclass of
ZulipTestCase, which will provide
you helper methods like the following:
For certain Zulip library functions, especially the ones that are not intrinsically tied to Django, we use a classic unit testing approach of calling the function and inspecting the results.
For these types of tests, you will often use methods like
self.assertTrue(), etc., which come with
Particularly for testing Zulip’s integrations with third party systems, we strive to have a highly data-driven approach to testing. To give a specific example, when we test our GitHub integration, the test code reads a bunch of sample inputs from a JSON fixture file, feeds them to our GitHub integration code, and then verifies the output against expected values from the same JSON fixture file.
Our fixtures live in
Mocks and stubs¶
We use mocks and stubs for all the typical reasons:
- to more precisely test the target code
- to stub out calls to third-party services
- to make it so that you can run your tests on the airplane without wifi
For mocking we generally use the “mock” library and use
a context manager or decorator. We also take advantage of some context managers
from Django as well as our own custom helpers. Here is an example:
with self.settings(RATE_LIMITING=True): with mock.patch('zerver.decorator.rate_limit_user') as rate_limit_mock: api_result = my_webhook(request) self.assertTrue(rate_limit_mock.called)
Follow this link for more information on the “settings” context manager.
In zerver/tests/test_templates.py we have a test that renders all of our back end templates with a “dummy” context, to make sure the templates don’t have obvious errors. (These tests won’t catch all types of errors; they are just a first line of defense.)
SQL performance tests¶
A common class of bug with Django systems is to handle bulk data in
an inefficient way, where the back end populates objects for join tables
with a series of individual queries that give O(N) latency. (The
remedy is often just to call
select_related(), but sometimes it
requires a more subtle restructuring of the code.)
We try to prevent these bugs in our tests by using a context manager
queries_captured() that captures the SQL queries used by
the back end during a particular operation. We make assertions about
those queries, often simply asserting that the number of queries is
below some threshold.
The Zulip back end has a mechanism where it will fetch initial data
for a client from the database, and then it will subsequently apply
some queued up events to that data to the data structure before notifying
the client. The
EventsRegisterTest.do_test() helper helps tests
verify that the application of those events via apply_events() produces
the same data structure as performing an action that generates said event.
This is a bit esoteric, but if you read the tests, you will see some of the patterns. You can also learn more about our event system in the new feature tutorial.
It is important to verify error handling paths for endpoints, particularly
situations where we need to ensure that we don’t return results to clients
with improper authentication or with limited authorization. A typical test
will call the endpoint with either a non-logged in client, an invalid API
key, or missing input fields. Then the test will call
to verify that the endpoint is properly failing.
Here are some things to consider when writing new tests:
- Duplication We try to avoid excessive duplication in tests. If you have several tests repeating the same type of test setup, consider making a setUp() method or a test helper.
- Network independence Our tests should still work if you don’t have an internet connection. For third party clients, you can simulate their behavior using fixture data. For third party servers, you can typically simulate their behavior using mocks.
- Coverage We have 100% line coverage on several of our backend
modules. You can use the
--coverageoption to generate coverage reports, and new code should have 100% coverage, which generally requires testing not only the “happy path” but also error handling code and edge cases. Note that
test-backend --coveragewill assert that various specific files in the project have 100% test coverage and throw an error if their coverage has fallen. One of our project goals is to expand that checking to ever-larger parts of the codebase.