Package: genty
AboutGenty, pronounced “gen-tee”, stands for “generate tests”. It promotes generative
testing, where a single test can execute over a variety of input. Genty makes
this a breeze.For example, consider a file sample.py containing both the code under test and
the tests:fromgentyimportgenty,genty_repeat,genty_datasetfromunittestimportTestCase# Here's the class under testclassMyClass(object):defadd_one(self,x):returnx+1# Here's the test code@gentyclassMyClassTests(TestCase):@genty_dataset((0,1),(100000,100001),)deftest_add_one(self,value,expected_result):actual_result=MyClass().add_one(value)self.assertEqual(expected_result,actual_result)Running theMyClassTestsusing the default unittest runner$python-munittest-vsampletest_add_one(0, 1) (sample.MyClassTests) ... ok
test_add_one(100000, 100001) (sample.MyClassTests) ... ok

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Ran 2 tests in 0.000s

OKInstead of having to write multiple independent tests for various test cases,
code can be refactored and parametrized using genty!It produces readable tests.
It produces maintainable tests.
It produces expressive tests.Another option is running the same test multiple times. This is useful in stress
tests or when exercising code looking for race conditions. This particular test@genty_repeat(3)deftest_adding_one_to_zero(self):self.assertEqual(1,MyClass().add_one(0))would be run 3 times, producing output like$python-munittest-vsampletest_adding_one() iteration_1 (sample.MyClassTests) ... ok
test_adding_one() iteration_2 (sample.MyClassTests) ... ok
test_adding_one() iteration_3 (sample.MyClassTests) ... ok

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Ran 3 tests in 0.001s

OKThe 2 techniques can be combined:@genty_repeat(2)@genty_dataset((0,1),(100000,100001),)deftest_add_one(self,value,expected_result):actual_result=MyClass().add_one(value)self.assertEqual(expected_result,actual_result)There are more options to explore including naming your datasets andgenty_args.@genty_dataset(default_case=(0,1),limit_case=(999,1000),error_case=genty_args(-1,-1,is_something=False),)deftest_complex(self,value1,value2,optional_value=None,is_something=True):...would run 3 tests, producing output like$python-munittest-vsampletest_complex(default_case) (sample.MyClassTests) ... ok
test_complex(limit_case) (sample.MyClassTests) ... ok
test_complex(error_case) (sample.MyClassTests) ... ok

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Ran 3 tests in 0.003s

OKThe@genty_datasetscan be chained together. This is useful, for example, if there are semantically different datasets
so keeping them separate would help expressiveness.@genty_dataset(10,100)@genty_dataset('first','second')deftest_composing(self,parameter_value):...would run 4 tests, producing output like$python-munittest-vsampletest_composing(10) (sample.MyClassTests) ... ok
test_composing(100) (sample.MyClassTests) ... ok
test_composing(u'first') (sample.MyClassTests) ... ok
test_composing(u'second') (sample.MyClassTests) ... ok

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Ran 4 tests in 0.000s

OKSometimes the parameters to a test can’t be determined at module load time. For example,
some test might be based on results from some http request. And first the test needs to
authenticate, etc. This is supported using the@genty_dataproviderdecorator like so:defsetUp(self):super(MyClassTests,self).setUp()# http authentication happens# And imagine that _some_function is actually some http requestself._some_function=lambdax,y:((x+y),(x-y),(x*y))@genty_dataset((1000,100),(100,1))defcalculate(self,x_val,y_val):# when this is called... we've been authenticatedreturnself._some_function(x_val,y_val)@genty_dataprovider(calculate)deftest_heavy(self,data1,data2,data3):...would run 4 tests, producing output like$python-munittest-vsampletest_heavy_calculate(100, 1) (sample.MyClassTests) ... ok
test_heavy_calculate(1000, 100) (sample.MyClassTests) ... ok

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Ran 2 tests in 0.000s

OKNotice here how the name of the helper (calculate) is added to the names of the 2
executed test cases.Like@genty_dataset,@genty_dataprovidercan be chained together.Enjoy!Deferred ParameterizationParametrized tests where the final parameters are not determined until test
execution time. It looks like so:@genty_dataset((1000,100),(100,1))defcalculate(self,x_val,y_val):# when this is called... we've been authenticated, perhaps in# some Test.setUp() method.# Let's imagine that _some_function requires that authentication.# And it returns a 3-tuple, matching that signature of# of the test(s) decorated with this 'calculate' method.returnself._some_function(x_val,y_val)@genty_dataprovider(calculate)deftest_heavy(self,data1,data2,data3):...Thecalculate()method is called 2 times based on the@genty_datasetdecorator, and each of it’s return values define the final parameters that will
be given to the methodtest_heavy(...).InstallationTo install, simply:pip install gentyContributingSeeCONTRIBUTING.rst.SetupCreate a virtual environment and install packages -mkvirtualenv genty
pip install -r requirements-dev.txtTestingRun all tests using -toxThe tox tests include code style checks via pep8 and pylint.The tox tests are configured to run on Python 2.6, 2.7, 3.3, 3.4, 3.5, and
PyPy 2.6.Copyright and LicenseCopyright 2015 Box, Inc. All rights reserved.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

   http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.