Example usage

DDT consists of a class decorator ddt (for your TestCase subclass) and two method decorators (for your tests that want to be multiplied):

• data: contains as many arguments as values you want to feed to the test.
• file_data: will load test data from a JSON file.

Normally each value within data will be passed as a single argument to your test method. If these values are e.g. tuples, you will have to unpack them inside your test. Alternatively, you can use an additional decorator, unpack, that will automatically unpack tuples and lists into multiple arguments, and dictionaries into multiple keyword arguments. See examples below.

This allows you to write your tests as:

import unittest
from ddt import ddt, data, file_data, unpack
from test.mycode import larger_than_two, has_three_elements, is_a_greeting


class Mylist(list):
    pass


def annotated(a, b):
    r = Mylist([a, b])
    setattr(r, "__name__", "test_%d_greater_than_%d" % (a, b))
    return r


@ddt
class FooTestCase(unittest.TestCase):
    def test_undecorated(self):
        self.assertTrue(larger_than_two(24))

    @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))

    @data(annotated(2, 1), annotated(10, 5))
    def test_greater(self, value):
        a, b = value
        self.assertGreater(a, b)

    @file_data('test_data_dict.json')
    def test_file_data_dict(self, value):
        self.assertTrue(has_three_elements(value))

    @file_data('test_data_list.json')
    def test_file_data_list(self, value):
        self.assertTrue(is_a_greeting(value))

    @data((3, 2), (4, 3), (5, 3))
    @unpack
    def test_tuples_extracted_into_arguments(self, first_value, second_value):
        self.assertTrue(first_value > second_value)

    @data([3, 2], [4, 3], [5, 3])
    @unpack
    def test_list_extracted_into_arguments(self, first_value, second_value):
        self.assertTrue(first_value > second_value)

    @unpack
    @data({'first': 1, 'second': 3, 'third': 2},
          {'first': 4, 'second': 6, 'third': 5})
    def test_dicts_extracted_into_kwargs(self, first, second, third):
        self.assertTrue(first < third < second)

    @data(u'ascii', u'non-ascii-\N{SNOWMAN}')
    def test_unicode(self, value):
        self.assertIn(value, (u'ascii', u'non-ascii-\N{SNOWMAN}'))

Where test_data_dict.json:

{
    "unsorted_list": [ 10, 12, 15 ],
    "sorted_list": [ 15, 12, 50 ]
}

and test_data_list.json:

[
    "Hello",
    "Goodbye"
]

And then run them with your favourite test runner, e.g. if you use nose:

$ nosetests -v test/test_example.py

The number of test cases actually run and reported separately has been multiplied.

DDT will try to give the new test cases meaningful names by converting the data values to valid python identifiers.

Note

Python 2.7.3 introduced hash randomization which is by default enabled on Python 3.3 and later. DDT’s default mechanism to generate meaningful test names will not use the test data value as part of the name for complex types if hash randomization is enabled.

You can disable hash randomization by setting the PYTHONHASHSEED environment variable to a fixed value before running tests (export PYTHONHASHSEED=1 for example).