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Backend Django tests

Overview

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.

Running tests

Our tests live in zerver/tests/. You can run them with ./tools/test-backend. The tests run in parallel using multiple threads in your development environment, and can finish in under 30s on a fast machine. 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.test_queue_worker.WorkerTest

There are many command line options for running Zulip tests, such as a --verbose option. The best way to learn the options is to use the online help:

./tools/test-backend -h

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 tests.

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 --nonfatal-errors option. A useful option to combine with that is the --rerun option, which will rerun just the tests that failed in the last test run.

Writing 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 in 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.

Another important files to skim are zerver/lib/test_helpers.py, which contains test helpers. zerver/lib/test_classes.py, which contains our ZulipTestCase and WebhookTestCase classes.

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 tools/setup/generate-fixtures.)

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:

common_subscribe_to_streams
send_message
make_stream
subscribe_to_stream

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:

check_send_message
do_change_is_admin
do_create_user
do_make_stream_private

Testing with mocks

This section is a beginner's guide to mocking with Python's unittest.mock library. It will give you answers to the most common questions around mocking, and a selection of commonly used mocking techniques.

What is mocking?

When writing tests, mocks allow you to replace methods or objects with fake entities suiting your testing requirements. Once an object is mocked, its original code does not get executed anymore.

Rather, you can think of a mocked object as an initially empty shell: Calling it won't do anything, but you can fill your shell with custom code, return values, etc. Additionally, you can observe any calls made to your mocked object.

Why is mocking useful?

When writing tests, it often occurs that you make calls to functions taking complex arguments. Creating a real instance of such an argument would require the use of various different libraries, a lot of boilerplate code, etc. Another scenario is that the tested code accesses files or objects that don't exist at testing time. Finally, it is good practice to keep tests independent from others. Mocks help you to isolate test cases by simulating objects and methods irrelevant to a test's goal.

In all of these cases, you can "mock out" the function calls / objects and replace them with fake instances that only implement a limited interface. On top of that, these fake instances can be easily analyzed.

Say you have a method greet(name_key) defined as follows:

def greet(name_key: str) -> str:
    name = fetch_database(name_key)
    return "Hello " + name

You want to test greet().

In your test, you want to call greet("Mario") and verify that it returns the correct greeting:

  def test_greet() -> str:
      greeting = greet("Mario")
      assert greeting == "Hello Mr. Mario Mario"

-> You have a problem: greet() calls fetch_database(). fetch_database() does some look-ups in a database. You haven't created that database for your tests, so your test would fail, even though the code is correct.

Luckily, you know that fetch_database("Mario") should return "Mr. Mario Mario".
- Hint: Sometimes, you might not know the exact return value, but one that is equally valid and works with the rest of the code. In that case, just use this one.

-> Solution: You mock fetch_database(). This is also referred to as "mocking out" fetch_database().

from unittest.mock import MagickMock # Our mocking class that will replace `fetch_database()`

def test_greet() -> None:
    # Mock `fetch_database()` with an object that acts like a shell: It still accepts calls like `fetch_database()`,
    # but doesn't do any database lookup. We "fill" the shell with a return value; This value will be returned on every
    # call to `fetch_database()`.
    fetch_database = MagicMock(return_value="Mr. Mario Mario")
    greeting = greet("Mario")
    assert greeting == "Hello Mr. Mario Mario"

That's all. Note that this mock is suitable for testing greet(), but not for testing fetch_database(). More generally, you should only mock those functions you explicitly don't want to test.

How does mocking work under the hood?

Since Python 3.3, the standard mocking library is unittest.mock. unittest.mock implements the basic mocking class Mock. It also implements MagickMock, which is the same as Mock, but contains many default magic methods (in Python, those are the ones starting with with a dunder __). From the docs:

In most of these examples the Mock and MagicMock classes are interchangeable. As the MagicMock is the more capable class it makes a sensible one to use by default.

Mock itself is a class that principally accepts and records any and all calls. A piece of code like

from unittest import mock

foo = mock.Mock()
foo.bar('quux')
foo.baz
foo.qux = 42

is not going to throw any errors. Our mock silently accepts all these calls and records them. Mock also implements methods for us to access and assert its records, e.g.

foo.bar.assert_called_with('quux')

Finally, unittest.mock also provides a method to mock objects only within a scope: patch(). We can use patch() either as a decorator or as a context manager. In both cases, the mock created by patch() will apply for the scope of the decorator / context manager. patch() takes only one required argument target. target is a string in dot notation that refers to the name of the object you want to mock. It will then assign a MagickMock() to that object. As an example, look at the following code:

from unittest import mock
from os import urandom

with mock.patch('__main__.urandom', return_value=42):
    print(urandom(1))
    print(urandom(1)) # No matter what value we plug in for urandom, it will always return 42.
print(urandom(1)) # We exited the context manager, so the mock doesn't apply anymore. Will return a random byte.

Note that calling mock.patch('os.urandom', return_value=42) wouldn't work here: os.urandom would be the name of our patched object. However, we imported urandom with from os import urandom; hence, we bound the urandom name to our current module __main__.

On the other hand, if we had used import os.urandom, we would need to call mock.patch('os.urandom', return_value=42) instead.

Boilerplate code

Including the Python mocking library:
```
from unittest import mock
```

Mocking a class with a context manager:

with mock.patch('module.ClassName', foo=42, return_value='I am a mock') as my_mock:
  # In here, 'module.ClassName' is mocked with a MagicMock() object my_mock.
  # my_mock has an attribute named foo with the value 42.
  # var = module.ClassName() will assign 'I am a mock' to var.

Mocking a class with a decorator:

@mock.patch('module.ClassName', foo=42, return_value='I am a mock')
def my_function(my_mock):
    # ...
    # In here, 'module.ClassName' will behave as in the previous example.

Mocking a class attribute:

with mock.patch.object(module.ClassName, 'class_method', return_value=42)
  # In here, 'module.ClassName' has the same properties as before, except for 'class_method'
  # Calling module.ClassName.class_method() will now return 42.

Note the missing quotes around module.ClassName in the patch.object() call.

Zulip mocking practices

For mocking we generally use the "mock" library and use mock.patch as 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.

A common use is to prevent a call to a third-party service from using the Internet; git grep mock.patch | grep requests is a good way to find several examples of doing this.

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...

Endpoint tests

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:

api_auth
assert_json_error
assert_json_success
client_get
client_post
get_api_key
get_streams
login
send_message

Library tests

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.assertEqual(), self.assertTrue(), etc., which come with unittest via Django.

Fixture-driven tests

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 zerver/fixtures.

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 the Zulip tests on the airplane without wifi

A detailed description of mocks, along with useful coded snippets, can be found in the section Testing with mocks.

Template tests

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 called 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.

Event-based tests

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.

Negative tests

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 assert_json_error() to verify that the endpoint is properly failing.

Testing considerations

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 --coverage option 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. It will generate a nice HTML report that you can view right from your browser (the tool prints the URL where the report is exposed in your development environment).

Note that test-backend --coverage will 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.

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