This page documents the latest, unreleased version of Buildbot. For documentation for released versions, see http://docs.buildbot.net/current/.
3.1.5. Buildbot's Test Suite¶
Buildbot's master tests are under
buildbot-worker package tests are under
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.
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 . .venv/bin/activate # now we 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.test_reporters_mail
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 testing 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 to act as a failsafe for the unit and interface tests.
- Regression tests (
buildbot.test.regressions) - these test to prevent re-occurrence of historical bugs. In most cases, a regression is better tested by a test in the other suites, or unlike 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.
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 are be named after the package or class they test, replacing
_ and omitting the
buildbot_. For example,
Periodic class in
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 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 you renderables:
def setUp(self): self.build = Properties(paramVal='val') @defer.inlineCallbacks def test_param_renderable(self): f = self.ConcreteClass(param=Interpolate('%(kw:rendered_val)s', rendered_val=Property('paramVal')) yield f.start_instance(self.build) self.assertEqual(f.param, 'val')
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_db_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.
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.,
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.
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 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 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
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
Buildbot provides a number of purpose-specific mixin classes in master/buildbot/util.
These generally define a set of utility functions as well as
These methods should be called explicitly from your subclass's
Note that some of these methods return Deferreds, which should be handled properly by the caller.
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.
184.108.40.206. 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
- Data API dictionaries, as returned from
- Data API messages, and
- Simple data types.
These are validated from elsewhere in the codebase with calls to
verifyDbDict(testcase, type, value),
verifyData(testcase, type, options, value),
verifyMessage(testcase, routingKey, message), and
verifyType(testcase, name, value, validator).
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
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.
A validator is an instance of the
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.
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:
NoneOkwraps another validator, allowing the object to be None.
Anywill match any object without error.
IdentifierValidatorwill match identifiers; see identifier.
DictValidatortakes key names as keyword arguments, with the values giving validators for each key. The
optionalNamesargument is a list of keys which may be omitted without error.
SourcedPropertiesValidatormatches dictionaries with (value, source) keys, the representation used for properties in the data API.
MessageValidatorvalidates 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
eventsset. The remaining "middle" tuple elements must match the message values identified by
messageValidatorshould be a
DictValidatorconfigured to check the message body. This validator's
validatemethod is called with a tuple
Selectorallows different validators to be selected based on matching functions. Its
addmethod 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.
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
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
message['foo'] = Selector() message['foo'].add(lambda rk : rk[-1] == 'new', MessageValidator( keyFields=['fooid'], events=['new', 'complete'], messageValidator=DictValidator( fooid=IntValidator(), name=StringValidator(), ... )))
220.127.116.11. 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 good test?
Independent of Time¶
Tests that depend on wall time will fail. As a bonus, they run very slowly. Do
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!
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.
Test method names should follow the pattern
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.
The shorter each 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.
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.
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).
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.
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!
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
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.