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This third-party package's source repository does not contain a package manifest. Instead, its package manifest is stored in its release repository. In order to build this package from source in a Catkin workspace, please download its package manifest.Package Summary
| Tags | No category tags. |
| Version | 1.3.0 |
| License | Apache |
| Build type | CATKIN |
| Use | RECOMMENDED |
Repository Summary
| Checkout URI | https://github.com/box/genty.git |
| VCS Type | git |
| VCS Version | v1.3.0 |
| Last Updated | 2015-11-06 |
| Dev Status | MAINTAINED |
| CI status | No Continuous Integration |
| Released | RELEASED |
| Tags | No category tags. |
| Contributing |
Help Wanted (0)
Good First Issues (0) Pull Requests to Review (0) |
Package Description
Additional Links
Maintainers
- AlexV
Authors
genty
About
Genty, 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:
``` {.sourceCode .python} from genty import genty, genty_repeat, genty_dataset from unittest import TestCase
Here’s the class under test
class MyClass(object): def add_one(self, x): return x + 1
Here’s the test code
@genty class MyClassTests(TestCase): @genty_dataset( (0, 1), (100000, 100001), ) def test_add_one(self, value, expected_result): actual_result = MyClass().add_one(value) self.assertEqual(expected_result, actual_result)
Running the `MyClassTests` using the default unittest runner
``` {.sourceCode .console}
$ python -m unittest -v sample
test_add_one(0, 1) (sample.MyClassTests) ... ok
test_add_one(100000, 100001) (sample.MyClassTests) ... ok
----------------------------------------------------------------------
Ran 2 tests in 0.000s
OK
Instead 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
``` {.sourceCode .python} @genty_repeat(3) def test_adding_one_to_zero(self): self.assertEqual(1, MyClass().add_one(0))
would be run 3 times, producing output like
``` {.sourceCode .console}
$ python -m unittest -v sample
test_adding_one() iteration_1 (sample.MyClassTests) ... ok
test_adding_one() iteration_2 (sample.MyClassTests) ... ok
test_adding_one() iteration_3 (sample.MyClassTests) ... ok
----------------------------------------------------------------------
Ran 3 tests in 0.001s
OK
The 2 techniques can be combined:
``` {.sourceCode .python} @genty_repeat(2) @genty_dataset( (0, 1), (100000, 100001), ) def test_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 and
`genty_args`.
``` {.sourceCode .python}
@genty_dataset(
default_case=(0, 1),
limit_case=(999, 1000),
error_case=genty_args(-1, -1, is_something=False),
)
def test_complex(self, value1, value2, optional_value=None, is_something=True):
...
would run 3 tests, producing output like
``` {.sourceCode .console} $ python -m unittest -v sample test_complex(default_case) (sample.MyClassTests) … ok test_complex(limit_case) (sample.MyClassTests) … ok test_complex(error_case) (sample.MyClassTests) … ok
Ran 3 tests in 0.003s
OK
The `@genty_datasets` can be chained together. This is useful, for
example, if there are semantically different datasets so keeping them
separate would help expressiveness.
``` {.sourceCode .python}
@genty_dataset(10, 100)
@genty_dataset('first', 'second')
def test_composing(self, parameter_value):
...
would run 4 tests, producing output like
``` {.sourceCode .console} $ python -m unittest -v sample test_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
Ran 4 tests in 0.000s
OK
Sometimes 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_dataprovider` decorator like so:
``` {.sourceCode .python}
def setUp(self):
super(MyClassTests, self).setUp()
# http authentication happens
# And imagine that _some_function is actually some http request
self._some_function = lambda x, y: ((x + y), (x - y), (x * y))
@genty_dataset((1000, 100), (100, 1))
def calculate(self, x_val, y_val):
# when this is called... we've been authenticated
return self._some_function(x_val, y_val)
@genty_dataprovider(calculate)
def test_heavy(self, data1, data2, data3):
...
would run 4 tests, producing output like
``` {.sourceCode .console} $ python -m unittest -v sample test_heavy_calculate(100, 1) (sample.MyClassTests) … ok test_heavy_calculate(1000, 100) (sample.MyClassTests) … ok
Ran 2 tests in 0.000s
OK
Notice here how the name of the helper (`calculate`) is added to the
names of the 2 executed test cases.
Like `@genty_dataset`, `@genty_dataprovider` can be chained together.
Enjoy!
Deferred Parameterization
-------------------------
Parametrized tests where the final parameters are not determined until
test execution time. It looks like so:
``` {.sourceCode .python}
@genty_dataset((1000, 100), (100, 1))
def calculate(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.
return self._some_function(x_val, y_val)
@genty_dataprovider(calculate)
def test_heavy(self, data1, data2, data3):
...
The calculate() method is called 2 times based on the @genty_dataset
decorator, and each of it's return values define the final parameters
that will be given to the method test_heavy(...).
Installation
To install, simply:
``` {.sourceCode .console} pip install genty
Contributing
------------
See
[CONTRIBUTING.rst](https://github.com/box/genty/blob/master/CONTRIBUTING.rst).
### Setup
Create a virtual environment and install packages -
``` {.sourceCode .console}
mkvirtualenv genty
pip install -r requirements-dev.txt
Testing
Run all tests using -
``` {.sourceCode .console} tox
```
The 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 License
Copyright 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.
Wiki Tutorials
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Third-Party Package
This third-party package's source repository does not contain a package manifest. Instead, its package manifest is stored in its release repository. In order to build this package from source in a Catkin workspace, please download its package manifest.Package Summary
| Tags | No category tags. |
| Version | 1.3.0 |
| License | Apache |
| Build type | CATKIN |
| Use | RECOMMENDED |
Repository Summary
| Checkout URI | https://github.com/box/genty.git |
| VCS Type | git |
| VCS Version | v1.3.0 |
| Last Updated | 2015-11-06 |
| Dev Status | MAINTAINED |
| CI status | No Continuous Integration |
| Released | RELEASED |
| Tags | No category tags. |
| Contributing |
Help Wanted (0)
Good First Issues (0) Pull Requests to Review (0) |
Package Description
Additional Links
Maintainers
- AlexV
Authors
genty
About
Genty, 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:
``` {.sourceCode .python} from genty import genty, genty_repeat, genty_dataset from unittest import TestCase
Here’s the class under test
class MyClass(object): def add_one(self, x): return x + 1
Here’s the test code
@genty class MyClassTests(TestCase): @genty_dataset( (0, 1), (100000, 100001), ) def test_add_one(self, value, expected_result): actual_result = MyClass().add_one(value) self.assertEqual(expected_result, actual_result)
Running the `MyClassTests` using the default unittest runner
``` {.sourceCode .console}
$ python -m unittest -v sample
test_add_one(0, 1) (sample.MyClassTests) ... ok
test_add_one(100000, 100001) (sample.MyClassTests) ... ok
----------------------------------------------------------------------
Ran 2 tests in 0.000s
OK
Instead 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
``` {.sourceCode .python} @genty_repeat(3) def test_adding_one_to_zero(self): self.assertEqual(1, MyClass().add_one(0))
would be run 3 times, producing output like
``` {.sourceCode .console}
$ python -m unittest -v sample
test_adding_one() iteration_1 (sample.MyClassTests) ... ok
test_adding_one() iteration_2 (sample.MyClassTests) ... ok
test_adding_one() iteration_3 (sample.MyClassTests) ... ok
----------------------------------------------------------------------
Ran 3 tests in 0.001s
OK
The 2 techniques can be combined:
``` {.sourceCode .python} @genty_repeat(2) @genty_dataset( (0, 1), (100000, 100001), ) def test_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 and
`genty_args`.
``` {.sourceCode .python}
@genty_dataset(
default_case=(0, 1),
limit_case=(999, 1000),
error_case=genty_args(-1, -1, is_something=False),
)
def test_complex(self, value1, value2, optional_value=None, is_something=True):
...
would run 3 tests, producing output like
``` {.sourceCode .console} $ python -m unittest -v sample test_complex(default_case) (sample.MyClassTests) … ok test_complex(limit_case) (sample.MyClassTests) … ok test_complex(error_case) (sample.MyClassTests) … ok
Ran 3 tests in 0.003s
OK
The `@genty_datasets` can be chained together. This is useful, for
example, if there are semantically different datasets so keeping them
separate would help expressiveness.
``` {.sourceCode .python}
@genty_dataset(10, 100)
@genty_dataset('first', 'second')
def test_composing(self, parameter_value):
...
would run 4 tests, producing output like
``` {.sourceCode .console} $ python -m unittest -v sample test_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
Ran 4 tests in 0.000s
OK
Sometimes 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_dataprovider` decorator like so:
``` {.sourceCode .python}
def setUp(self):
super(MyClassTests, self).setUp()
# http authentication happens
# And imagine that _some_function is actually some http request
self._some_function = lambda x, y: ((x + y), (x - y), (x * y))
@genty_dataset((1000, 100), (100, 1))
def calculate(self, x_val, y_val):
# when this is called... we've been authenticated
return self._some_function(x_val, y_val)
@genty_dataprovider(calculate)
def test_heavy(self, data1, data2, data3):
...
would run 4 tests, producing output like
``` {.sourceCode .console} $ python -m unittest -v sample test_heavy_calculate(100, 1) (sample.MyClassTests) … ok test_heavy_calculate(1000, 100) (sample.MyClassTests) … ok
Ran 2 tests in 0.000s
OK
Notice here how the name of the helper (`calculate`) is added to the
names of the 2 executed test cases.
Like `@genty_dataset`, `@genty_dataprovider` can be chained together.
Enjoy!
Deferred Parameterization
-------------------------
Parametrized tests where the final parameters are not determined until
test execution time. It looks like so:
``` {.sourceCode .python}
@genty_dataset((1000, 100), (100, 1))
def calculate(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.
return self._some_function(x_val, y_val)
@genty_dataprovider(calculate)
def test_heavy(self, data1, data2, data3):
...
The calculate() method is called 2 times based on the @genty_dataset
decorator, and each of it's return values define the final parameters
that will be given to the method test_heavy(...).
Installation
To install, simply:
``` {.sourceCode .console} pip install genty
Contributing
------------
See
[CONTRIBUTING.rst](https://github.com/box/genty/blob/master/CONTRIBUTING.rst).
### Setup
Create a virtual environment and install packages -
``` {.sourceCode .console}
mkvirtualenv genty
pip install -r requirements-dev.txt
Testing
Run all tests using -
``` {.sourceCode .console} tox
```
The 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 License
Copyright 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.