This is an update to Doug Hellman's excellent PyMOTW article found here:
The code and examples here have been updated by Corey Goldberg to reflect Python 3.3.
further reading:
Python's unittest module, sometimes referred to as 'PyUnit', is based on the XUnit framework design by Kent Beck and Erich Gamma. The same pattern is repeated in many other languages, including C, Perl, Java, and Smalltalk. The framework implemented by unittest supports fixtures, test suites, and a test runner to enable automated testing for your code.
Tests, as defined by unittest, have two parts: code to manage test "fixtures", and the test itself. Individual tests are created by subclassing TestCase and overriding or adding appropriate methods. For example,
import unittest
class SimplisticTest(unittest.TestCase):
def test(self):
self.assertTrue(True)
if __name__ == '__main__':
unittest.main()In this case, the SimplisticTest has a single test() method, which would fail if True is ever False.
The easiest way to run unittest tests is to include:
if __name__ == '__main__':
unittest.main()at the bottom of each test file, then simply run the script directly from the command line:
$ python3 test_simple.py
.
----------------------------------------------------------------------
Ran 1 test in 0.000s
OK
This abbreviated output includes the amount of time the tests took, along with a status indicator for each test (the "." on the first line of output means that a test passed). For more detailed test results, include the -v option:
$ python3 test_simple.py -v
test (__main__.SimplisticTest) ... ok
----------------------------------------------------------------------
Ran 1 test in 0.000s
OK
Tests have 3 possible outcomes:
ok
The test passes.
FAIL
The test does not pass, and raises an AssertionError exception.
ERROR
The test raises an exception other than AssertionError.
There is no explicit way to cause a test to "pass", so a test's status depends on the presence (or absence) of an exception.
import unittest
class OutcomesTest(unittest.TestCase):
def test_pass(self):
self.assertTrue(True)
def test_fail(self):
self.assertTrue(False)
def test_error(self):
raise RuntimeError('Test error!')
if __name__ == '__main__':
unittest.main()When a test fails or generates an error, the traceback is included in the output.
$ python3 test_outcomes.py
EF.
======================================================================
ERROR: test_error (__main__.OutcomesTest)
----------------------------------------------------------------------
Traceback (most recent call last):
File "test_outcomes.py", line 13, in test_error
raise RuntimeError('Test error!')
RuntimeError: Test error!
======================================================================
FAIL: test_fail (__main__.OutcomesTest)
----------------------------------------------------------------------
Traceback (most recent call last):
File "test_outcomes.py", line 9, in test_fail
self.assertTrue(False)
AssertionError: False is not true
----------------------------------------------------------------------
Ran 3 tests in 0.000s
FAILED (failures=1, errors=1)
In the example above, test_fail() fails and the traceback shows the line with the failure code. It is up to the person reading the test output to look at the code to figure out the semantic meaning of the failed test, though. To make it easier to understand the nature of a test failure, the assert*() methods all accept an argument msg, which can be used to produce a more detailed error message.
import unittest
class FailureMessageTest(unittest.TestCase):
def test_fail(self):
self.assertTrue(False, 'failure message goes here')
if __name__ == '__main__':
unittest.main()$ python3 test_failwithmessage.py -v
test_fail (__main__.FailureMessageTest) ... FAIL
======================================================================
FAIL: test_fail (__main__.FailureMessageTest)
----------------------------------------------------------------------
Traceback (most recent call last):
File "test_failwithmessage.py", line 6, in test_fail
self.assertTrue(False, 'failure message goes here')
AssertionError: failure message goes here
----------------------------------------------------------------------
Ran 1 test in 0.000s
FAILED (failures=1)
Most tests assert the truth of some condition. There are a few different ways to write truth-checking tests, depending on the perspective of the test author and the desired outcome of the code being tested. If the code produces a value which can be evaluated as true, the method assertTrue() should be used. If the code produces a false value, the method assertFalse() makes more sense.
import unittest
class TruthTest(unittest.TestCase):
def test_assert_true(self):
self.assertTrue(True)
def test_assert_false(self):
self.assertFalse(False)
if __name__ == '__main__':
unittest.main()$ python3 test_truth.py -v
test_assert_false (__main__.TruthTest) ... ok
test_assert_true (__main__.TruthTest) ... ok
----------------------------------------------------------------------
Ran 2 tests in 0.000s
OK
The TestCase class provides a number of methods to check for and report failures:
| Method |
|---|
assertTrue(x, msg=None) |
assertFalse(x, msg=None) |
assertIsNone(x, msg=None) |
assertIsNotNone(x, msg=None) |
assertEqual(a, b, msg=None) |
assertNotEqual(a, b, msg=None) |
assertIs(a, b, msg=None) |
assertIsNot(a, b, msg=None) |
assertIn(a, b, msg=None) |
assertNotIn(a, b, msg=None) |
assertIsInstance(a, b, msg=None) |
assertNotIsInstance(a, b, msg=None) |
| Method |
|---|
assertAlmostEqual(a, b, places=7, msg=None, delta=None) |
assertNotAlmostEqual(a, b, places=7, msg=None, delta=None) |
assertGreater(a, b, msg=None) |
assertGreaterEqual(a, b, msg=None) |
assertLess(a, b, msg=None) |
assertLessEqual(a, b, msg=None) |
assertRegex(text, regexp, msg=None) |
assertNotRegex(text, regexp, msg=None) |
assertCountEqual(a, b, msg=None) |
assertMultiLineEqual(a, b, msg=None) |
assertSequenceEqual(a, b, msg=None) |
assertListEqual(a, b, msg=None) |
assertTupleEqual(a, b, msg=None) |
assertSetEqual(a, b, msg=None) |
assertDictEqual(a, b, msg=None) |
These assertions are handy, since the values being compared appear in the failure message when a test fails.
import unittest
class InequalityTest(unittest.TestCase):
def testEqual(self):
self.assertNotEqual(1, 3-2)
def testNotEqual(self):
self.assertEqual(2, 3-2)
if __name__ == '__main__':
unittest.main()And when these tests are run:
$ python3 test_notequal.py -v
testEqual (__main__.InequalityTest) ... FAIL
testNotEqual (__main__.InequalityTest) ... FAIL
======================================================================
FAIL: test_equal (__main__.InequalityTest)
----------------------------------------------------------------------
Traceback (most recent call last):
File "test_notequal.py", line 7, in test_equal
self.assertNotEqual(1, 3-2)
AssertionError: 1 == 1
======================================================================
FAIL: test_not_equal (__main__.InequalityTest)
----------------------------------------------------------------------
Traceback (most recent call last):
File "test_notequal.py", line 10, in test_not_equal
self.assertEqual(2, 3-2)
AssertionError: 2 != 1
----------------------------------------------------------------------
Ran 2 tests in 0.000s
FAILED (failures=2)
All the assert methods above accept a msg argument that, if specified, is used as the error message on failure.
The TestCase class provides methods to check for expected exceptions:
| Method |
|---|
assertRaises(exception) |
assertRaisesRegex(exception, regexp) |
assertWarns(warn, fun, *args, **kwds) |
assertWarnsRegex(warn, fun, *args, **kwds) |
As previously mentioned, if a test raises an exception other than AssertionError it is treated as an error. This is very useful for uncovering mistakes while you are modifying code which has existing test coverage. There are circumstances, however, in which you want the test to verify that some code does produce an exception. For example, if an invalid value is given to an attribute of an object. In such cases, assertRaises() makes the code more clear than trapping the exception yourself. Compare these two tests:
import unittest
def raises_error(*args, **kwds):
raise ValueError('Invalid value: %s%s' % (args, kwds))
class ExceptionTest(unittest.TestCase):
def test_trap_locally(self):
try:
raises_error('a', b='c')
except ValueError:
pass
else:
self.fail('Did not see ValueError')
def test_assert_raises(self):
self.assertRaises(ValueError, raises_error, 'a', b='c')
if __name__ == '__main__':
unittest.main()The results for both are the same, but the second test using assertRaises() is more succinct.
$ python3 test_exception.py -v
test_assert_raises (__main__.ExceptionTest) ... ok
test_trap_locally (__main__.ExceptionTest) ... ok
----------------------------------------------------------------------
Ran 2 tests in 0.000s
OK
Fixtures are resources needed by a test. For example, if you are writing several tests for the same class, those tests all need an instance of that class to use for testing. Other test fixtures include database connections and temporary files (many people would argue that using external resources makes such tests not "unit" tests, but they are still tests and still useful). TestCase includes a special hook to configure and clean up any fixtures needed by your tests. To configure the fixtures, override setUp(). To clean up, override tearDown().
import unittest
class FixturesTest(unittest.TestCase):
def setUp(self):
print('In setUp()')
self.fixture = range(1, 10)
def tearDown(self):
print('In tearDown()')
del self.fixture
def test(self):
print('in test()')
self.assertEqual(self.fixture, range(1, 10))
if __name__ == '__main__':
unittest.main()When this sample test is run, you can see the order of execution of the fixture and test methods:
$ python3 test_fixtures.py
.
----------------------------------------------------------------------
Ran 1 test in 0.000s
OK
In setUp()
in test()
In tearDown()
The standard library documentation describes how to organize test suites manually. I generally do not use test suites directly, because I prefer to build the suites automatically (these are automated tests, after all). Automating the construction of test suites is especially useful for large code bases, in which related tests are not all in the same place. Tools such as nose make it easier to manage tests when they are spread over multiple files and directories.