351 lines
12 KiB
Plaintext
351 lines
12 KiB
Plaintext
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.. _paramexamples:
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Parametrizing tests
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=================================================
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.. currentmodule:: _pytest.python
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py.test allows to easily parametrize test functions.
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For basic docs, see :ref:`parametrize-basics`.
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In the following we provide some examples using
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the builtin mechanisms.
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Generating parameters combinations, depending on command line
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----------------------------------------------------------------------------
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.. regendoc:wipe
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Let's say we want to execute a test with different computation
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parameters and the parameter range shall be determined by a command
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line argument. Let's first write a simple (do-nothing) computation test::
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# content of test_compute.py
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def test_compute(param1):
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assert param1 < 4
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Now we add a test configuration like this::
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# content of conftest.py
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def pytest_addoption(parser):
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parser.addoption("--all", action="store_true",
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help="run all combinations")
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def pytest_generate_tests(metafunc):
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if 'param1' in metafunc.fixturenames:
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if metafunc.config.option.all:
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end = 5
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else:
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end = 2
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metafunc.parametrize("param1", range(end))
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This means that we only run 2 tests if we do not pass ``--all``::
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$ py.test -q test_compute.py
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..
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We run only two computations, so we see two dots.
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let's run the full monty::
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$ py.test -q --all
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....F
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================================= FAILURES =================================
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_____________________________ test_compute[4] ______________________________
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param1 = 4
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def test_compute(param1):
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> assert param1 < 4
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E assert 4 < 4
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test_compute.py:3: AssertionError
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As expected when running the full range of ``param1`` values
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we'll get an error on the last one.
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A quick port of "testscenarios"
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------------------------------------
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.. _`test scenarios`: http://pypi.python.org/pypi/testscenarios/
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Here is a quick port to run tests configured with `test scenarios`_,
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an add-on from Robert Collins for the standard unittest framework. We
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only have to work a bit to construct the correct arguments for pytest's
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:py:func:`Metafunc.parametrize`::
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# content of test_scenarios.py
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def pytest_generate_tests(metafunc):
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idlist = []
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argvalues = []
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for scenario in metafunc.cls.scenarios:
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idlist.append(scenario[0])
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items = scenario[1].items()
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argnames = [x[0] for x in items]
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argvalues.append(([x[1] for x in items]))
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metafunc.parametrize(argnames, argvalues, ids=idlist, scope="class")
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scenario1 = ('basic', {'attribute': 'value'})
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scenario2 = ('advanced', {'attribute': 'value2'})
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class TestSampleWithScenarios:
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scenarios = [scenario1, scenario2]
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def test_demo1(self, attribute):
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assert isinstance(attribute, str)
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def test_demo2(self, attribute):
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assert isinstance(attribute, str)
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this is a fully self-contained example which you can run with::
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$ py.test test_scenarios.py
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=========================== test session starts ============================
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platform linux2 -- Python 2.7.3 -- pytest-2.3.5
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collected 4 items
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test_scenarios.py ....
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========================= 4 passed in 0.01 seconds =========================
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If you just collect tests you'll also nicely see 'advanced' and 'basic' as variants for the test function::
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$ py.test --collect-only test_scenarios.py
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=========================== test session starts ============================
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platform linux2 -- Python 2.7.3 -- pytest-2.3.5
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collected 4 items
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<Module 'test_scenarios.py'>
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<Class 'TestSampleWithScenarios'>
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<Instance '()'>
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<Function 'test_demo1[basic]'>
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<Function 'test_demo2[basic]'>
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<Function 'test_demo1[advanced]'>
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<Function 'test_demo2[advanced]'>
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============================= in 0.01 seconds =============================
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Note that we told ``metafunc.parametrize()`` that your scenario values
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should be considered class-scoped. With pytest-2.3 this leads to a
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resource-based ordering.
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Deferring the setup of parametrized resources
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---------------------------------------------------
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.. regendoc:wipe
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The parametrization of test functions happens at collection
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time. It is a good idea to setup expensive resources like DB
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connections or subprocess only when the actual test is run.
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Here is a simple example how you can achieve that, first
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the actual test requiring a ``db`` object::
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# content of test_backends.py
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import pytest
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def test_db_initialized(db):
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# a dummy test
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if db.__class__.__name__ == "DB2":
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pytest.fail("deliberately failing for demo purposes")
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We can now add a test configuration that generates two invocations of
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the ``test_db_initialized`` function and also implements a factory that
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creates a database object for the actual test invocations::
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# content of conftest.py
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import pytest
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def pytest_generate_tests(metafunc):
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if 'db' in metafunc.fixturenames:
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metafunc.parametrize("db", ['d1', 'd2'], indirect=True)
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class DB1:
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"one database object"
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class DB2:
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"alternative database object"
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@pytest.fixture
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def db(request):
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if request.param == "d1":
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return DB1()
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elif request.param == "d2":
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return DB2()
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else:
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raise ValueError("invalid internal test config")
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Let's first see how it looks like at collection time::
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$ py.test test_backends.py --collect-only
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=========================== test session starts ============================
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platform linux2 -- Python 2.7.3 -- pytest-2.3.5
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collected 2 items
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<Module 'test_backends.py'>
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<Function 'test_db_initialized[d1]'>
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<Function 'test_db_initialized[d2]'>
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============================= in 0.00 seconds =============================
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And then when we run the test::
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$ py.test -q test_backends.py
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.F
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================================= FAILURES =================================
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_________________________ test_db_initialized[d2] __________________________
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db = <conftest.DB2 instance at 0x2038f80>
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def test_db_initialized(db):
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# a dummy test
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if db.__class__.__name__ == "DB2":
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> pytest.fail("deliberately failing for demo purposes")
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E Failed: deliberately failing for demo purposes
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test_backends.py:6: Failed
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The first invocation with ``db == "DB1"`` passed while the second with ``db == "DB2"`` failed. Our ``db`` fixture function has instantiated each of the DB values during the setup phase while the ``pytest_generate_tests`` generated two according calls to the ``test_db_initialized`` during the collection phase.
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.. regendoc:wipe
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Parametrizing test methods through per-class configuration
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--------------------------------------------------------------
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.. _`unittest parameterizer`: http://code.google.com/p/unittest-ext/source/browse/trunk/params.py
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Here is an example ``pytest_generate_function`` function implementing a
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parametrization scheme similar to Michael Foord's `unittest
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parameterizer`_ but in a lot less code::
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# content of ./test_parametrize.py
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import pytest
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def pytest_generate_tests(metafunc):
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# called once per each test function
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funcarglist = metafunc.cls.params[metafunc.function.__name__]
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argnames = list(funcarglist[0])
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metafunc.parametrize(argnames, [[funcargs[name] for name in argnames]
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for funcargs in funcarglist])
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class TestClass:
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# a map specifying multiple argument sets for a test method
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params = {
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'test_equals': [dict(a=1, b=2), dict(a=3, b=3), ],
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'test_zerodivision': [dict(a=1, b=0), ],
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}
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def test_equals(self, a, b):
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assert a == b
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def test_zerodivision(self, a, b):
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pytest.raises(ZeroDivisionError, "a/b")
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Our test generator looks up a class-level definition which specifies which
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argument sets to use for each test function. Let's run it::
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$ py.test -q
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F..
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================================= FAILURES =================================
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________________________ TestClass.test_equals[1-2] ________________________
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self = <test_parametrize.TestClass instance at 0x1338f80>, a = 1, b = 2
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def test_equals(self, a, b):
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> assert a == b
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E assert 1 == 2
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test_parametrize.py:18: AssertionError
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Indirect parametrization with multiple fixtures
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--------------------------------------------------------------
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Here is a stripped down real-life example of using parametrized
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testing for testing serialization of objects between different python
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interpreters. We define a ``test_basic_objects`` function which
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is to be run with different sets of arguments for its three arguments:
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* ``python1``: first python interpreter, run to pickle-dump an object to a file
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* ``python2``: second interpreter, run to pickle-load an object from a file
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* ``obj``: object to be dumped/loaded
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.. literalinclude:: multipython.py
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Running it results in some skips if we don't have all the python interpreters installed and otherwise runs all combinations (5 interpreters times 5 interpreters times 3 objects to serialize/deserialize)::
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. $ py.test -rs -q multipython.py
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............sss............sss............sss............ssssssssssssssssss
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========================= short test summary info ==========================
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SKIP [27] /home/hpk/p/pytest/doc/en/example/multipython.py:21: 'python2.8' not found
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Indirect parametrization of optional implementations/imports
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--------------------------------------------------------------------
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If you want to compare the outcomes of several implementations of a given
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API, you can write test functions that receive the already imported implementations
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and get skipped in case the implementation is not importable/available. Let's
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say we have a "base" implementation and the other (possibly optimized ones)
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need to provide similar results::
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# content of conftest.py
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import pytest
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@pytest.fixture(scope="session")
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def basemod(request):
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return pytest.importorskip("base")
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@pytest.fixture(scope="session", params=["opt1", "opt2"])
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def optmod(request):
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return pytest.importorskip(request.param)
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And then a base implementation of a simple function::
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# content of base.py
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def func1():
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return 1
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And an optimized version::
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# content of opt1.py
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def func1():
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return 1.0001
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And finally a little test module::
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# content of test_module.py
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def test_func1(basemod, optmod):
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assert round(basemod.func1(), 3) == round(optmod.func1(), 3)
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If you run this with reporting for skips enabled::
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$ py.test -rs test_module.py
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=========================== test session starts ============================
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platform linux2 -- Python 2.7.3 -- pytest-2.3.5
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collected 2 items
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test_module.py .s
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========================= short test summary info ==========================
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SKIP [1] /tmp/doc-exec-275/conftest.py:10: could not import 'opt2'
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=================== 1 passed, 1 skipped in 0.01 seconds ====================
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You'll see that we don't have a ``opt2`` module and thus the second test run
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of our ``test_func1`` was skipped. A few notes:
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- the fixture functions in the ``conftest.py`` file are "session-scoped" because we
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don't need to import more than once
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- if you have multiple test functions and a skipped import, you will see
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the ``[1]`` count increasing in the report
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- you can put :ref:`@pytest.mark.parametrize <@pytest.mark.parametrize>` style
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parametrization on the test functions to parametrize input/output
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values as well.
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