pyUnittestExtras
extra functionalities for unittests
Classes and Objects
DataSet
DataSet is a collection of data (strings, lists, dictionaries) that are provided as sets to a test method for data driven testing
DataSource
DataSource provides the same data sets to a test method like a DataSet, but it takes the content from a local/remote file
DataProvider
DataProvider is a unittest test method decorator for data driven tests
DataProvider provides data sets to the test parameters via a DataSet or DataSource
Will result in multiple test versions of the decorated test method
DataProviderSingleton
Similar to DataProvider, but will not result in multiple test versions of the test method. Instead all data sets will report as a single test
Tutorial I: Providing Primitives
In its simplistic form, a DataSet can provide individual (primitive or not) parameters to test methods:
strings = DataSet(
'string one',
'string two',
'yet another string'
)These strings Dataset can then be provided to a test method using a DataProvider:
from unittest import TestCase
from unittestextras import DataProvider, DataSet
class my_test_class(TestCase):
strings = DataSet(
'string one',
'string two',
'yet another string'
)
@DataProvider(strings)
def test_string_has_3_words(self, text):
self.assertEqual(
len(text.split()), 3,
'string "%s" does not have 3 words' % text
)This will result in multiple test runs (3 in this example)
Output (removed Traceback info for simplicity):
pyUnittestExtras# python -m unittest discover -s test -p my_test_class.py
FF.
======================================================================
FAIL: test_string_has_3_words('string one') (test_temp.my_test_class)
----------------------------------------------------------------------
Traceback (most recent call last):
...
AssertionError: string "string one" does not have 3 words
======================================================================
FAIL: test_string_has_3_words('string two') (test_temp.my_test_class)
----------------------------------------------------------------------
Traceback (most recent call last):
...
AssertionError: string "string two" does not have 3 words
----------------------------------------------------------------------
Ran 3 tests in 0.000s
FAILED (failures=2)Tutorial II: Providing Lists
DataSet can also provide lists and tuples to test methods that are expecting more than one parameter:
from unittest import TestCase
from unittestextras import DataProvider, DataSet
class my_test_class(TestCase):
strings_and_expected_lengths = DataSet(
["abcdefg", 7],
["x"*7, 7],
["abc"*4, 12]
)
@DataProvider(strings_and_expected_lengths)
def test_my_string(self, text, expected_length):
self.assertEqual(len(text), expected_length)Tutorial III: Providing Dictionaries
Similar to providing lists, DataSet can also provide keyed dictionaries,
this makes the data a bit more readable and easy to follow.
Caveat: the keys in the data dictionaries must match the named parameters of the test method.
from unittest import TestCase
from unittestextras import DataProvider, DataSet
class my_test_class(TestCase):
strings_and_expected_lengths = DataSet(
dict(text="abcdefg", expected_length=7),
dict(text="x"*7, expected_length=7),
dict(text="abc"*4, expected_length=12)
)
@DataProvider(strings_and_expected_lengths)
def test_my_string(self, text, expected_length):
self.assertEqual(len(text), expected_length)Tutorial IV: DataProviderSingleton
While DataProvider creates an individual result per data set provided, DataProviderSingleton combines
all data sets provided into a single test result (though test setup and teardown will occur between variations).
from unittest import TestCase
from unittestextras import DataProviderSingleton as DataProvider, DataSet
class my_test_class(TestCase):
strings_and_expected_lengths = DataSet(
dict(text="abcdefg", expected_length=4), #will fail
dict(text="x"*7, expected_length=7),
dict(text="abc"*4, expected_length=11) #will fail
)
@DataProvider(strings_and_expected_lengths)
def test_my_string(self, text, expected_length):
self.assertEqual(len(text), expected_length)will result in the following output:
pyUnittestExtras# python -m unittest discover -s . -p test_sample.py -v
test_my_string (test_sample.my_test_class) ... FAIL
======================================================================
FAIL: test_my_string (test_sample.my_test_class)
----------------------------------------------------------------------
Traceback (most recent call last):
...
raise assertion
AssertionError: The following Assertions occurred: ['Data set [0]: 7 != 4', 'Data set [2]: 12 != 11']
----------------------------------------------------------------------
Ran 1 test in 0.001s
FAILED (failures=1)Notice that only 1 test is tracked by unittest module here!
Much like DataProvider, id_index and id_key can be used here as well to make the failing
results easier to track to the individual data set.
Appendix: Identifying Results
By default tests will be named in reference to their entire passed data set, but it's also possible to customize the signature of the individual test results.
Naming results by indexed data parameter:
DataProvider allows using one of the passed parameters in each set as a 'label' for the test id in the results by setting a id_index:
from unittest import TestCase
from unittestextras import DataProvider, DataSet
class my_test_class(TestCase):
strings_and_expected_lengths = DataSet(
["Simple String", "abcdefg", 7],
["Multiplied Character", "x"*7, 7],
["Multipled String", "abc"*4, 11]
)
@DataProvider(strings_and_expected_lengths, id_index=0)
def test_my_string(self, data_label, text, expected_length):
self.assertEqual(len(text), expected_length)results in:
FAIL: test_my_string(Multipled String) (test_temp.my_test_class)Which is a bit more readable than (without id_index:
FAIL: test_my_string('Multipled String','abcabcabcabc',11) (test_temp.my_test_class)Naming results by keyed data parameter:
DataProvider also allows customizing the test signature in the results using a keyed parameter when passing keyed dictionaries in the DataSet by setting the id_key:
from unittest import TestCase
from unittestextras import DataProvider, DataSet
class my_test_class(TestCase):
strings_and_expected_lengths = DataSet(
dict(data_label="Simple String", text="abcdefg", expected_length=7),
dict(data_label="Multiplied Character", text="x"*7, expected_length=6),
dict(data_label="Multiplied String", text="abc"*4, expected_length=12)
)
@DataProvider(strings_and_expected_lengths, id_key='data_label')
def test_my_string(self, data_label, text, expected_length):
self.assertEqual(len(text), expected_length)Results in:
FAIL: test_my_string(Multiplied Character) (test_temp.my_test_class)Which is a bit more readable than (without id_key):
FAIL: test_my_string(expected_length=6,text=xxxxxxx,data_label=Multiplied Character) (test_temp.my_test_class)
