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I have some kind of test data and want to create a unit test for each item. My first idea was to do it like this:
import unittest
l = [["foo", "a", "a",], ["bar", "a", "b"], ["lee", "b", "b"]]
class TestSequence(unittest.TestCase):
def testsample(self):
for name, a,b in l:
print "test", name
self.assertEqual(a,b)
if __name__ == '__main__':
unittest.main()
The downside of this is that it handles all data in one test. I would like to generate one test for each item on the fly. Any suggestions?
Answer #1:
This is called “parametrization”.
There are several tools that support this approach. E.g.:
The resulting code looks like this:
from parameterized import parameterized
class TestSequence(unittest.TestCase):
@parameterized.expand([
["foo", "a", "a",],
["bar", "a", "b"],
["lee", "b", "b"],
])
def test_sequence(self, name, a, b):
self.assertEqual(a,b)
Which will generate the tests:
test_sequence_0_foo (__main__.TestSequence) ... ok
test_sequence_1_bar (__main__.TestSequence) ... FAIL
test_sequence_2_lee (__main__.TestSequence) ... ok
======================================================================
FAIL: test_sequence_1_bar (__main__.TestSequence)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/usr/local/lib/python2.7/site-packages/parameterized/parameterized.py", line 233, in <lambda>
standalone_func = lambda *a: func(*(a + p.args), **p.kwargs)
File "x.py", line 12, in test_sequence
self.assertEqual(a,b)
AssertionError: 'a' != 'b'
For historical reasons I’ll leave the original answer circa 2008):
I use something like this:
import unittest
l = [["foo", "a", "a",], ["bar", "a", "b"], ["lee", "b", "b"]]
class TestSequense(unittest.TestCase):
pass
def test_generator(a, b):
def test(self):
self.assertEqual(a,b)
return test
if __name__ == '__main__':
for t in l:
test_name = 'test_%s' % t[0]
test = test_generator(t[1], t[2])
setattr(TestSequense, test_name, test)
unittest.main()
Answer #2:
Using unittest (since 3.4)
Since Python 3.4, the standard library unittest package has the subTest context manager.
See the documentation:
Example:
from unittest import TestCase
param_list = [('a', 'a'), ('a', 'b'), ('b', 'b')]
class TestDemonstrateSubtest(TestCase):
def test_works_as_expected(self):
for p1, p2 in param_list:
with self.subTest():
self.assertEqual(p1, p2)
You can also specify a custom message and parameter values to subTest():
with self.subTest(msg="Checking if p1 equals p2", p1=p1, p2=p2):
Using nose
The nose testing framework supports this.
Example (the code below is the entire contents of the file containing the test):
param_list = [('a', 'a'), ('a', 'b'), ('b', 'b')]
def test_generator():
for params in param_list:
yield check_em, params[0], params[1]
def check_em(a, b):
assert a == b
The output of the nosetests command:
> nosetests -v
testgen.test_generator('a', 'a') ... ok
testgen.test_generator('a', 'b') ... FAIL
testgen.test_generator('b', 'b') ... ok
======================================================================
FAIL: testgen.test_generator('a', 'b')
----------------------------------------------------------------------
Traceback (most recent call last):
File "/usr/lib/python2.5/site-packages/nose-0.10.1-py2.5.egg/nose/case.py", line 203, in runTest
self.test(*self.arg)
File "testgen.py", line 7, in check_em
assert a == b
AssertionError
----------------------------------------------------------------------
Ran 3 tests in 0.006s
FAILED (failures=1)
Answer #3:
This can be solved elegantly using Metaclasses:
import unittest
l = [["foo", "a", "a",], ["bar", "a", "b"], ["lee", "b", "b"]]
class TestSequenceMeta(type):
def __new__(mcs, name, bases, dict):
def gen_test(a, b):
def test(self):
self.assertEqual(a, b)
return test
for tname, a, b in l:
test_name = "test_%s" % tname
dict[test_name] = gen_test(a,b)
return type.__new__(mcs, name, bases, dict)
class TestSequence(unittest.TestCase):
__metaclass__ = TestSequenceMeta
if __name__ == '__main__':
unittest.main()
Answer #4:
As of Python 3.4, subtests have been introduced to unittest for this purpose. See the documentation for details. TestCase.subTest is a context manager which allows one to isolate asserts in a test so that a failure will be reported with parameter information, but it does not stop the test execution. Here’s the example from the documentation:
class NumbersTest(unittest.TestCase):
def test_even(self):
"""
Test that numbers between 0 and 5 are all even.
"""
for i in range(0, 6):
with self.subTest(i=i):
self.assertEqual(i % 2, 0)
The output of a test run would be:
======================================================================
FAIL: test_even (__main__.NumbersTest) (i=1)
----------------------------------------------------------------------
Traceback (most recent call last):
File "subtests.py", line 32, in test_even
self.assertEqual(i % 2, 0)
AssertionError: 1 != 0
======================================================================
FAIL: test_even (__main__.NumbersTest) (i=3)
----------------------------------------------------------------------
Traceback (most recent call last):
File "subtests.py", line 32, in test_even
self.assertEqual(i % 2, 0)
AssertionError: 1 != 0
======================================================================
FAIL: test_even (__main__.NumbersTest) (i=5)
----------------------------------------------------------------------
Traceback (most recent call last):
File "subtests.py", line 32, in test_even
self.assertEqual(i % 2, 0)
AssertionError: 1 != 0
This is also part of unittest2, so it is available for earlier versions of Python.
Answer #5:
load_tests is a little known mechanism introduced in 2.7 to dynamically create a TestSuite. With it, you can easily create parametrized tests.
For example:
import unittest
class GeneralTestCase(unittest.TestCase):
def __init__(self, methodName, param1=None, param2=None):
super(GeneralTestCase, self).__init__(methodName)
self.param1 = param1
self.param2 = param2
def runTest(self):
pass # Test that depends on param 1 and 2.
def load_tests(loader, tests, pattern):
test_cases = unittest.TestSuite()
for p1, p2 in [(1, 2), (3, 4)]:
test_cases.addTest(GeneralTestCase('runTest', p1, p2))
return test_cases
That code will run all the TestCases in the TestSuite returned by load_tests. No other tests are automatically run by the discovery mechanism.
Alternatively, you can also use inheritance as shown in this ticket: http://bugs.python.org/msg151444
Answer #6:
It can be done by using pytest. Just write the file test_me.py with content:
import pytest
@pytest.mark.parametrize('name, left, right', [['foo', 'a', 'a'],
['bar', 'a', 'b'],
['baz', 'b', 'b']])
def test_me(name, left, right):
assert left == right, name
And run your test with command py.test --tb=short test_me.py. Then the output will look like:
=========================== test session starts ============================
platform darwin -- Python 2.7.6 -- py-1.4.23 -- pytest-2.6.1
collected 3 items
test_me.py .F.
================================= FAILURES =================================
_____________________________ test_me[bar-a-b] _____________________________
test_me.py:8: in test_me
assert left == right, name
E AssertionError: bar
==================== 1 failed, 2 passed in 0.01 seconds ====================
It is simple! Also pytest has more features like fixtures, mark, assert, etc.
Answer #7:
Use the ddt library. It adds simple decorators for the test methods:
import unittest
from ddt import ddt, data
from mycode import larger_than_two
@ddt
class FooTestCase(unittest.TestCase):
@data(3, 4, 12, 23)
def test_larger_than_two(self, value):
self.assertTrue(larger_than_two(value))
@data(1, -3, 2, 0)
def test_not_larger_than_two(self, value):
self.assertFalse(larger_than_two(value))
This library can be installed with pip. It doesn’t require nose, and works excellent with the standard library unittest module.
Answer #8:
You would benefit from trying the TestScenarios library.
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).
