# 3.3.3. Buildbot’s Test Suite

Buildbot’s master tests are under `buildbot.test` and `buildbot-worker` package tests are under `buildbot_worker.test`. Tests for the workers are similar to the master, although in some cases helpful functionality on the master is not re-implemented on the worker.

## 3.3.3.1. Quick-Start

Buildbot uses Twisted trial to run its test suite. Following is a quick shell session to put you on the right track.

    # the usual buildbot development bootstrap with git and virtualenv
    git clone https://github.com/buildbot/buildbot
    cd buildbot

    # helper script which creates the virtualenv for development
    make virtualenv
    . .venvpython3/bin/activate

    # now run the test suite
    trial buildbot

    # find all tests that talk about mail
    trial -n --reporter=bwverbose buildbot | grep mail

    # run only one test module
    trial buildbot.test.unit.reporters.test_mail.TestMailNotifier

## 3.3.3.2. Suites

Tests are divided into a few suites:

- Unit tests (`buildbot.test.unit`) - these follow unit-testing practices and attempt to maximally isolate the system under test. Unit tests are the main mechanism of achieving test coverage, and all new code should be well-covered by corresponding unit tests.

  - Interface tests are a special type of unit tests, and are found in the same directory and often the same file. In many cases, Buildbot has multiple implementations of the same interface – at least one “real” implementation and a fake implementation used in unit testing. The interface tests ensure that these implementations all meet the same standards. This ensures consistency between implementations, and also ensures that the unit tests are tested against realistic fakes.

- Integration tests (`buildbot.test.integration`) - these test combinations of multiple units. Of necessity, integration tests are incomplete - they cannot test every condition; difficult to maintain - they tend to be complex and touch a lot of code; and slow - they usually require considerable setup and execute a lot of code. As such, use of integration tests is limited to a few broad tests that act as a failsafe for the unit and interface tests.

- Regression tests (`buildbot.test.regressions`) - these tests are used to prevent re-occurrence of historical bugs. In most cases, a regression is better tested by a test in the other suites, or is unlikely to recur, so this suite tends to be small.

- Fuzz tests (`buildbot.test.fuzz`) - these tests run for a long time and apply randomization to try to reproduce rare or unusual failures. The Buildbot project does not currently have a framework to run fuzz tests regularly.

### Unit Tests

Every code module should have corresponding unit tests. This is not currently true of Buildbot, due to a large body of legacy code, but is a goal of the project. All new code must meet this requirement.

Unit test modules follow the source file hierarchy (omitting the root `buildbot` directory) and are named after the package or class they test (replacing `.` with `_`). For example, test_timed_Periodic.py tests the `Periodic` class in master/buildbot/schedulers/timed.py. Modules with only one class, or a few trivial classes, can be tested in a single test module. For more complex situations, prefer to use multiple test modules.

Unit tests using renderables require special handling. The following example shows how the same test would be written with the ‘param’ parameter as a plain argument and with the same parameter as a renderable:

    def test_param(self):
        f = self.ConcreteClass(param='val')
        self.assertEqual(f.param, 'val')

When the parameter is renderable, you need to instantiate the class before you can test the renderables:

    def setUp(self):
        self.build = Properties(param='val')

    @defer.inlineCallbacks
    def test_param_renderable(self):
        f = self.ConcreteClass(
            param=Interpolate('%(kw:rendered_val)s',
                              rendered_val=Property('param')))
        yield f.start_instance(self.build)
        self.assertEqual(f.param, 'val')

### Interface Tests

Interface tests exist to verify that multiple implementations of an interface meet the same requirements. Note that the name ‘interface’ should not be confused with the sparse use of Zope Interfaces in the Buildbot code – in this context, an interface is any boundary between testable units.

Ideally, all interfaces, both public and private, should be tested. Certainly, any public interfaces need interface tests.

Interface tests are most often found in files named for the “real” implementation, e.g., test_changes.py. When there is ambiguity, test modules should be named after the interface they are testing. Interface tests have the following form:

    from buildbot.test.util import interfaces
    from twistd.trial import unittest

    class Tests(interfaces.InterfaceTests):

        # define methods that must be overridden per implementation
        def someSetupMethod(self):
            raise NotImplementedError

        # method signature tests
        def test_signature_someMethod(self):
            @self.assertArgSpecMatches(self.systemUnderTest.someMethod)
            def someMethod(self, arg1, arg2):
                pass

        # tests that all implementations must pass
        def test_something(self):
            pass # ...

    class RealTests(Tests):

        # tests that only *real* implementations must pass
        def test_something_else(self):
            pass # ...

All of the test methods are defined here, segregated into tests that all implementations must pass, and tests that the fake implementation is not expected to pass. The `test_signature_someMethod` test above illustrates the `buildbot.test.util.interfaces.assertArgSpecMatches` decorator, which can be used to compare the argument specification of a callable with a reference signature conveniently written as a nested function. Wherever possible, prefer to add tests to the `Tests` class, even if this means testing one method (e.g,. `setFoo`) in terms of another (e.g., `getFoo`).

The `assertArgSpecMatches` method can take multiple methods to test; it will check each one in turn.

At the bottom of the test module, a subclass is created for each implementation, implementing the setup methods that were stubbed out in the parent classes:

    class TestFakeThing(unittest.TestCase, Tests):

        def someSetupMethod(self):
            pass # ...

    class TestRealThing(unittest.TestCase, RealTests):

        def someSetupMethod(self):
            pass # ...

For implementations which require optional software, such as an AMQP server, this is the appropriate place to signal that tests should be skipped when their prerequisites are not available:

    from twisted.trial import unittest

    class TestRealThing(unittest.TestCase, RealTests):

        def someSetupMethod(self):
            try:
                import foo
            except ImportError:
                raise unittest.SkipTest("foo not found")

### Integration Tests

Integration test modules test several units at once, including their interactions. In general, they serve as a catch-all for failures and bugs that were not detected by the unit and interface tests. As such, they should not aim to be exhaustive, but merely representative.

Integration tests are very difficult to maintain if they reach into the internals of any part of Buildbot. Where possible, try to use the same means as a user would to set up, run, and check the results of an integration test. That may mean writing a `master.cfg` to be parsed, and checking the results by examining the database (or fake DB API) afterward.

### Regression Tests

Regression tests are even more rare in Buildbot than integration tests. In many cases, a regression test is not necessary – either the test is better-suited as a unit or interface test, or the failure is so specific that a test will never fail again.

Regression tests tend to be closely tied to the code in which the error occurred. When that code is refactored, the regression test generally becomes obsolete, and is deleted.

### Fuzz Tests

Fuzz tests generally run for a fixed amount of time, running randomized tests against a system. They do not run at all during normal runs of the Buildbot tests, unless `BUILDBOT_FUZZ` is defined. This is accomplished with something like the following at the end of each test module:

    if 'BUILDBOT_FUZZ' not in os.environ:
        del LRUCacheFuzzer

## 3.3.3.3. Mixins

Buildbot provides a number of purpose-specific mixin classes in master/buildbot/util. These generally define a set of utility functions as well as `setUpXxx` and `tearDownXxx` methods. These methods should be called explicitly from your subclass’s `setUp` and `tearDown` methods. Note that some of these methods return Deferreds, which should be handled properly by the caller.

## 3.3.3.4. Fakes

Buildbot provides a number of pre-defined fake implementations of internal interfaces, in master/buildbot/test/fake. These are designed to be used in unit tests to limit the scope of the test. For example, the fake DB API eliminates the need to create a real database when testing code that uses the DB API, and isolates bugs in the system under test from bugs in the real DB implementation.

The danger of using fakes is that the fake interface and the real interface can differ. The interface tests exist to solve this problem. All fakes should be fully tested in an integration test, so that the fakes pass the same tests as the “real” thing. It is particularly important that the method signatures be compared.

## 3.3.3.5. Type Validation

The master/buildbot/test/util/validation.py provides a set of classes and definitions for validating Buildbot data types. It supports four types of data:

> - DB API dictionaries, as returned from the `getXxx` methods,

> - Data API dictionaries, as returned from `get`,

> - Data API messages, and

> - Simple data types.

These are validated from elsewhere in the codebase with calls to

> - `verifyData(testcase, type, options, value)`,

> - `verifyMessage(testcase, routingKey, message)`, and

> - `verifyType(testcase, name, value, validator)`,

respectively. The `testcase` argument is used to fail the test case if the validation does not succeed. For DB dictionaries and data dictionaries, the `type` identifies the expected data type. For messages, the type is determined from the first element of the routing key.

All messages sent with the fake MQ implementation are automatically validated using `verifyMessage`. The `verifyType` method is used to validate simple types, e.g.,

    validation.verifyType(self, 'param1', param1, validation.StringValidator())

In any case, if `testcase` is None, then the functions will raise an `AssertionError` on failure.

### Validator Classes

A validator is an instance of the `Validator` class. Its `validate` method is a generator function that takes a name and an object to validate. It yields error messages describing any deviations of `object` from the designated data type. The `name` argument is used to make such messages more helpful.

A number of validators are supplied for basic types. A few classes deserve special mention:

> - `NoneOk` wraps another validator, allowing the object to be None.

> - `Any` will match any object without error.

> - `IdentifierValidator` will match identifiers; see identifier.

> - `DictValidator` takes key names as keyword arguments, with the values giving validators for each key. The `optionalNames` argument is a list of keys which may be omitted without error.

> - `SourcedPropertiesValidator` matches dictionaries with (value, source) keys, the representation used for properties in the data API.

> - `MessageValidator` validates messages. It checks that the routing key is a tuple of strings. The first tuple element gives the message type. The last tuple element is the event, and must be a member of the `events` set. The `messageValidator` should be a `DictValidator` configured to check the message body. This validator’s `validate` method is called with a tuple `(routingKey, message)`.

> - `Selector` allows different validators to be selected based on matching functions. Its `add` method takes a matching function, which should return a boolean, and a validator to use if the matching function returns true. If the matching function is None, it is used as a default. This class is used for message and data validation.

### Defining Validators

DB validators are defined in the `dbdict` dictionary, e.g.,

    dbdict['foodict'] = DictValidator(
        id=IntValidator(),
        name=StringValidator(),
        ...
    )

Data validators are `Selector` validators, where the selector is the `options` passed to `verifyData`.

    data['foo'] = Selector()
    data['foo'].add(lambda opts : opt.get('fanciness') > 10,
        DictValidator(
            fooid=IntValidator(),
            name=StringValidator(),
            ...
    ))

Similarly, message validators are `Selector` validators, where the selector is the routing key. The underlying validator should be a `MessageValidator`.

    message['foo'] = Selector()
    message['foo'].add(lambda rk : rk[-1] == 'new',
        MessageValidator(
            events=['new', 'complete'],
            messageValidator=DictValidator(
                fooid=IntValidator(),
                name=StringValidator(),
                ...
           )))

## 3.3.3.6. Good Tests

Bad tests are worse than no tests at all. Since they waste developers’ time wondering “was that a spurious failure?” or “what the heck is this test trying to do?”, Buildbot needs good tests. So what makes a test good?

### Independent of Time

Tests that depend on wall time will fail. As a bonus, they run very slowly. Do not use `reactor.callLater` to wait “long enough” for something to happen.

For testing things that themselves depend on time, consider using `twisted.internet.tasks.Clock`. This may mean passing a clock instance to the code under test, and propagating that instance as necessary to ensure that all of the code using `callLater` uses it. Refactoring code for testability is difficult, but worthwhile.

For testing things that do not depend on time, but for which you cannot detect the “end” of an operation: add a way to detect the end of the operation!

### Clean Code

Make your tests readable. This is no place to skimp on comments! Others will attempt to learn about the expected behavior of your class by reading the tests. As a side note, if you use a `Deferred` chain in your test, write the callbacks as nested functions, rather than using methods with funny names:

    def testSomething(self):
        d = doThisFirst()
        def andThisNext(res):
            pass # ...
        d.addCallback(andThisNext)
        return d

This isolates the entire test into one indented block. It is OK to add methods for common functionality, but give them real names and explain in detail what they do.

### Good Name

Test method names should follow the pattern `test_METHOD_CONDITION` where METHOD is the method being tested, and CONDITION is the condition under which it’s tested. Since we can’t always test a single method, this is not a hard-and-fast rule.

### Assert Only One Thing

Where practical, each test should have a single assertion. This may require a little bit of work to get several related pieces of information into a single Python object for comparison. The problem with multiple assertions is that, if the first assertion fails, the remainder are not tested. The test results then do not tell the entire story.

### Prefer Fakes to Mocks

Mock objects are too “compliant”, and this often masks errors in the system under test. For example, a mis-spelled method name on a mock object will not raise an exception.

Where possible, use one of the pre-written fake objects (see Fakes) instead of a mock object. Fakes themselves should be well-tested using interface tests.

Where they are appropriate, Mock objects can be constructed easily using the aptly-named mock module, which is a requirement for Buildbot’s tests.

### Small Tests

The shorter a test is, the better. Test as little code as possible in each test.

It is fine, and in fact encouraged, to write the code under test in such a way as to facilitate this. As an illustrative example, if you are testing a new Step subclass, but your tests require instantiating a BuildMaster, you’re probably doing something wrong!

This also applies to test modules. Several short, easily-digested test modules are preferred over a 1000-line monster.

### Isolation

Each test should be maximally independent of other tests. Do not leave files laying around after your test has finished, and do not assume that some other test has run beforehand. It’s fine to use caching techniques to avoid repeated, lengthy setup times.

### Be Correct

Tests should be as robust as possible, which at a basic level means using the available frameworks correctly. All Deferreds should have callbacks and be chained properly. Error conditions should be checked properly. Race conditions should not exist (see Independent of Time, above).

### Be Helpful

Note that tests will pass most of the time, but the moment when they are most useful is when they fail.

When the test fails, it should produce output that is helpful to the person chasing it down. This is particularly important when the tests are run remotely, in which case the person chasing down the bug does not have access to the system on which the test fails. A test which fails sporadically with no more information than “AssertionFailed” is a prime candidate for deletion if the error isn’t obvious. Making the error obvious also includes adding comments describing the ways a test might fail.

### Keeping State

Python does not allow assignment to anything but the innermost local scope or the global scope with the `global` keyword. This presents a problem when creating nested functions:

    def test_localVariable(self):
        cb_called = False
        def cb():
            cb_called = True
        cb()
        self.assertTrue(cb_called) # will fail!

The `cb_called = True` assigns to a different variable than `cb_called = False`. In production code, it’s usually best to work around such problems, but in tests this is often the clearest way to express the behavior under test.

The solution is to change something in a common mutable object. While a simple list can serve as such a mutable object, this leads to code that is hard to read. Instead, use `State`:

    from buildbot.test.state import State

    def test_localVariable(self):
        state = State(cb_called=False)
        def cb():
            state.cb_called = True
        cb()
        self.assertTrue(state.cb_called) # passes

This is almost as readable as the first example, but it actually works.