Testscenarios, a pyunit extension for dependency injection

## Project description

Copyright (c) 2009, Robert Collins <robertc@robertcollins.net>

Licensed under either the Apache License, Version 2.0 or the BSD 3-clause license at the users choice. A copy of both licenses are available in the project source as Apache-2.0 and BSD. You may not use this file except in compliance with one of these two licences.

Unless required by applicable law or agreed to in writing, software distributed under these licenses is distributed on an “AS IS” BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the license you chose for the specific language governing permissions and limitations under that license.

testscenarios provides clean dependency injection for python unittest style tests. This can be used for interface testing (testing many implementations via a single test suite) or for classic dependency injection (provide tests with dependencies externally to the test code itself, allowing easy testing in different situations).

## Why TestScenarios

Standard Python unittest.py provides on obvious method for running a single test_foo method with two (or more) scenarios: by creating a mix-in that provides the functions, objects or settings that make up the scenario. This is however limited and unsatisfying. Firstly, when two projects are cooperating on a test suite (for instance, a plugin to a larger project may want to run the standard tests for a given interface on its implementation), then it is easy for them to get out of sync with each other: when the list of TestCase classes to mix-in with changes, the plugin will either fail to run some tests or error trying to run deleted tests. Secondly, its not as easy to work with runtime-created-subclasses (a way of dealing with the aforementioned skew) because they require more indirection to locate the source of the test, and will often be ignored by e.g. pyflakes pylint etc.

It is the intent of testscenarios to make dynamically running a single test in multiple scenarios clear, easy to debug and work with even when the list of scenarios is dynamically generated.

## Defining Scenarios

A scenario is a tuple of a string name for the scenario, and a dict of parameters describing the scenario. The name is appended to the test name, and the parameters are made available to the test instance when it’s run.

Scenarios are presented in scenario lists which are typically Python lists but may be any iterable.

## Getting Scenarios applied

At its heart the concept is simple. For a given test object with a list of scenarios we prepare a new test object for each scenario. This involves:

• Clone the test to a new test with a new id uniquely distinguishing it.

• Apply the scenario to the test by setting each key, value in the scenario as attributes on the test object.

There are some complicating factors around making this happen seamlessly. These factors are in two areas:

• Choosing what scenarios to use. (See Setting Scenarios For A Test).

• Getting the multiplication to happen.

### Subclasssing

If you can subclass TestWithScenarios, then the run() method in TestWithScenarios will take care of test multiplication. It will at test execution act as a generator causing multiple tests to execute. For this to work reliably TestWithScenarios must be first in the MRO and you cannot override run() or __call__. This is the most robust method, in the sense that any test runner or test loader that obeys the python unittest protocol will run all your scenarios.

### Manual generation

If you cannot subclass TestWithScenarios (e.g. because you are using TwistedTestCase, or TestCaseWithResources, or any one of a number of other useful test base classes, or need to override run() or __call__ yourself) then you can cause scenario application to happen later by calling testscenarios.generate_scenarios(). For instance:

>>> import unittest
>>> try:
...     from StringIO import StringIO
... except ImportError:
...     from io import StringIO
>>> from testscenarios.scenarios import generate_scenarios

This can work with loaders and runners from the standard library, or possibly other implementations:

>>> loader = unittest.TestLoader()
>>> test_suite = unittest.TestSuite()
>>> runner = unittest.TextTestRunner(stream=StringIO())

>>> runner.run(test_suite)
<unittest...TextTestResult run=1 errors=0 failures=0>

Some test loaders support hooks like load_tests and test_suite. Ensuring your tests have had scenario application done through these hooks can be a good idea - it means that external test runners (which support these hooks like nose, trial, tribunal) will still run your scenarios. (Of course, if you are using the subclassing approach this is already a surety). With load_tests:

>>> def load_tests(standard_tests, module, loader):
...     return result

as a convenience, this is available in load_tests_apply_scenarios, so a module using scenario tests need only say

>>> from testscenarios import load_tests_apply_scenarios as load_tests

Python 2.7 and greater support a different calling convention for load_tests <https://bugs.launchpad.net/bzr/+bug/607412>. load_tests_apply_scenarios copes with both.

With test_suite:

>>> def test_suite():
...     return result

## Setting Scenarios for a test

A sample test using scenarios can be found in the doc/ folder.

See pydoc testscenarios for details.

### On the TestCase

You can set a scenarios attribute on the test case:

>>> class MyTest(unittest.TestCase):
...
...     scenarios = [
...         ('scenario1', dict(param=1)),
...         ('scenario2', dict(param=2)),]

This provides the main interface by which scenarios are found for a given test. Subclasses will inherit the scenarios (unless they override the attribute).

Test scenarios can also be generated arbitrarily later, as long as the test has not yet run. Simply replace (or alter, but be aware that many tests may share a single scenarios attribute) the scenarios attribute. For instance in this example some third party tests are extended to run with a custom scenario.

>>> import testtools
>>> class TestTransport:
...     """Hypothetical test case for bzrlib transport tests"""
...     pass
...
...     ['doc.test_sample'])
...
>>> for test in testtools.iterate_tests(stock_library_tests):
...     if isinstance(test, TestTransport):
...         test.scenarios = test.scenarios + [my_vfs_scenario]
...
>>> suite = unittest.TestSuite()
>>> suite.addTests(generate_scenarios(stock_library_tests))

Generated tests don’t have a scenarios list, because they don’t normally require any more expansion. However, you can add a scenarios list back on to them, and then run them through generate_scenarios again to generate the cross product of tests.

>>> class CrossProductDemo(unittest.TestCase):
...     scenarios = [('scenario_0_0', {}),
...                  ('scenario_0_1', {})]
...     def test_foo(self):
...         return
...
>>> suite = unittest.TestSuite()
>>> for test in testtools.iterate_tests(suite):
...     test.scenarios = [
...         ('scenario_1_0', {}),
...         ('scenario_1_1', {})]
...
>>> suite2 = unittest.TestSuite()
>>> print(suite2.countTestCases())
4

### Dynamic Scenarios

A common use case is to have the list of scenarios be dynamic based on plugins and available libraries. An easy way to do this is to provide a global scope scenarios somewhere relevant to the tests that will use it, and then that can be customised, or dynamically populate your scenarios from a registry etc. For instance:

>>> hash_scenarios = []
>>> try:
...     from hashlib import md5
... except ImportError:
...     pass
... else:
...     hash_scenarios.append(("md5", dict(hash=md5)))
>>> try:
...     from hashlib import sha1
... except ImportError:
...     pass
... else:
...     hash_scenarios.append(("sha1", dict(hash=sha1)))
...
>>> class TestHashContract(unittest.TestCase):
...
...     scenarios = hash_scenarios
...
>>> class TestHashPerformance(unittest.TestCase):
...
...     scenarios = hash_scenarios

### Forcing Scenarios

The apply_scenarios function can be useful to apply scenarios to a test that has none applied. apply_scenarios is the workhorse for generate_scenarios, except it takes the scenarios passed in rather than introspecting the test object to determine the scenarios. The apply_scenarios function does not reset the test scenarios attribute, allowing it to be used to layer scenarios without affecting existing scenario selection.

## Generating Scenarios

Some functions (currently one :-) are available to ease generation of scenario lists for common situations.

### Testing Per Implementation Module

It is reasonably common to have multiple Python modules that provide the same capabilities and interface, and to want apply the same tests to all of them.

In some cases, not all of the statically defined implementations will be able to be used in a particular testing environment. For example, there may be both a C and a pure-Python implementation of a module. You want to test the C module if it can be loaded, but also to have the tests pass if the C module has not been compiled.

The per_module_scenarios function generates a scenario for each named module. The module object of the imported module is set in the supplied attribute name of the resulting scenario. Modules which raise ImportError during import will have the sys.exc_info() of the exception set instead of the module object. Tests can check for the attribute being a tuple to decide what to do (e.g. to skip).

Note that for the test to be valid, all access to the module under test must go through the relevant attribute of the test object. If one of the implementations is also directly imported by the test module or any other, testscenarios will not magically stop it being used.

If a parameterised test is because of a bug run without being parameterized, it should fail rather than running with defaults, because this can hide bugs.

## Producing Scenarios

The multiply_scenarios function produces the cross-product of the scenarios passed in:

>>> from testscenarios.scenarios import multiply_scenarios
>>>
>>> scenarios = multiply_scenarios(
...      [('scenario1', dict(param1=1)), ('scenario2', dict(param1=2))],
...      [('scenario2', dict(param2=1))],
...      )
>>> scenarios == [('scenario1,scenario2', {'param2': 1, 'param1': 1}),
...               ('scenario2,scenario2', {'param2': 1, 'param1': 2})]
True

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