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allpairspy
Artifacts are distributable versions of a package. It typically includes the source code and metadata
Advanced tools
- Version
- 2.4.1
- Maintainers
- 1
.. contents:: allpairspy forked from bayandin/allpairs <https://github.com/bayandin/allpairs>__
:backlinks: top
:depth: 2
.. image:: https://badge.fury.io/py/allpairspy.svg :target: https://badge.fury.io/py/allpairspy :alt: PyPI package version
.. image:: https://img.shields.io/pypi/pyversions/allpairspy.svg :target: https://pypi.org/project/allpairspy :alt: Supported Python versions
.. image:: https://github.com/thombashi/allpairspy/workflows/Tests/badge.svg :target: https://github.com/thombashi/allpairspy/actions?query=workflow%3ATests :alt: Linux/macOS/Windows CI status
.. image:: https://coveralls.io/repos/github/thombashi/allpairspy/badge.svg?branch=master :target: https://coveralls.io/github/thombashi/allpairspy?branch=master :alt: Test coverage
AllPairs test combinations generator
AllPairs is an open source test combinations generator written in Python, developed and maintained by MetaCommunications Engineering. The generator allows one to create a set of tests using "pairwise combinations" method, reducing a number of combinations of variables into a lesser set that covers most situations.
For more info on pairwise testing see http://www.pairwise.org.
Features
- Produces good enough dataset.
- Pythonic, iterator-style enumeration interface.
- Allows to filter out "invalid" combinations during search for the next combination.
- Goes beyond pairs! If/when required can generate n-wise combinations.
Get Started
Basic Usage
:Sample Code: .. code:: python
from allpairspy import AllPairs
parameters = [
["Brand X", "Brand Y"],
["98", "NT", "2000", "XP"],
["Internal", "Modem"],
["Salaried", "Hourly", "Part-Time", "Contr."],
[6, 10, 15, 30, 60],
]
print("PAIRWISE:")
for i, pairs in enumerate(AllPairs(parameters)):
print("{:2d}: {}".format(i, pairs))
:Output: .. code::
PAIRWISE:
0: ['Brand X', '98', 'Internal', 'Salaried', 6]
1: ['Brand Y', 'NT', 'Modem', 'Hourly', 6]
2: ['Brand Y', '2000', 'Internal', 'Part-Time', 10]
3: ['Brand X', 'XP', 'Modem', 'Contr.', 10]
4: ['Brand X', '2000', 'Modem', 'Part-Time', 15]
5: ['Brand Y', 'XP', 'Internal', 'Hourly', 15]
6: ['Brand Y', '98', 'Modem', 'Salaried', 30]
7: ['Brand X', 'NT', 'Internal', 'Contr.', 30]
8: ['Brand X', '98', 'Internal', 'Hourly', 60]
9: ['Brand Y', '2000', 'Modem', 'Contr.', 60]
10: ['Brand Y', 'NT', 'Modem', 'Salaried', 60]
11: ['Brand Y', 'XP', 'Modem', 'Part-Time', 60]
12: ['Brand Y', '2000', 'Modem', 'Hourly', 30]
13: ['Brand Y', '98', 'Modem', 'Contr.', 15]
14: ['Brand Y', 'XP', 'Modem', 'Salaried', 15]
15: ['Brand Y', 'NT', 'Modem', 'Part-Time', 15]
16: ['Brand Y', 'XP', 'Modem', 'Part-Time', 30]
17: ['Brand Y', '98', 'Modem', 'Part-Time', 6]
18: ['Brand Y', '2000', 'Modem', 'Salaried', 6]
19: ['Brand Y', '98', 'Modem', 'Salaried', 10]
20: ['Brand Y', 'XP', 'Modem', 'Contr.', 6]
21: ['Brand Y', 'NT', 'Modem', 'Hourly', 10]
Filtering
You can restrict pairs by setting a filtering function to filter_func at
AllPairs constructor.
:Sample Code: .. code:: python
from allpairspy import AllPairs
def is_valid_combination(row):
"""
This is a filtering function. Filtering functions should return True
if combination is valid and False otherwise.
Test row that is passed here can be incomplete.
To prevent search for unnecessary items filtering function
is executed with found subset of data to validate it.
"""
n = len(row)
if n > 1:
# Brand Y does not support Windows 98
if "98" == row[1] and "Brand Y" == row[0]:
return False
# Brand X does not work with XP
if "XP" == row[1] and "Brand X" == row[0]:
return False
if n > 4:
# Contractors are billed in 30 min increments
if "Contr." == row[3] and row[4] < 30:
return False
return True
parameters = [
["Brand X", "Brand Y"],
["98", "NT", "2000", "XP"],
["Internal", "Modem"],
["Salaried", "Hourly", "Part-Time", "Contr."],
[6, 10, 15, 30, 60]
]
print("PAIRWISE:")
for i, pairs in enumerate(AllPairs(parameters, filter_func=is_valid_combination)):
print("{:2d}: {}".format(i, pairs))
:Output: .. code::
PAIRWISE:
0: ['Brand X', '98', 'Internal', 'Salaried', 6]
1: ['Brand Y', 'NT', 'Modem', 'Hourly', 6]
2: ['Brand Y', '2000', 'Internal', 'Part-Time', 10]
3: ['Brand X', '2000', 'Modem', 'Contr.', 30]
4: ['Brand X', 'NT', 'Internal', 'Contr.', 60]
5: ['Brand Y', 'XP', 'Modem', 'Salaried', 60]
6: ['Brand X', '98', 'Modem', 'Part-Time', 15]
7: ['Brand Y', 'XP', 'Internal', 'Hourly', 15]
8: ['Brand Y', 'NT', 'Internal', 'Part-Time', 30]
9: ['Brand X', '2000', 'Modem', 'Hourly', 10]
10: ['Brand Y', 'XP', 'Modem', 'Contr.', 30]
11: ['Brand Y', '2000', 'Modem', 'Salaried', 15]
12: ['Brand Y', 'NT', 'Modem', 'Salaried', 10]
13: ['Brand Y', 'XP', 'Modem', 'Part-Time', 6]
14: ['Brand Y', '2000', 'Modem', 'Contr.', 60]
Data Source: OrderedDict
You can use collections.OrderedDict instance as an argument for AllPairs constructor.
Pairs will be returned as collections.namedtuple instances.
:Sample Code: .. code:: python
from collections import OrderedDict
from allpairspy import AllPairs
parameters = OrderedDict({
"brand": ["Brand X", "Brand Y"],
"os": ["98", "NT", "2000", "XP"],
"minute": [15, 30, 60],
})
print("PAIRWISE:")
for i, pairs in enumerate(AllPairs(parameters)):
print("{:2d}: {}".format(i, pairs))
:Sample Code: .. code::
PAIRWISE:
0: Pairs(brand='Brand X', os='98', minute=15)
1: Pairs(brand='Brand Y', os='NT', minute=15)
2: Pairs(brand='Brand Y', os='2000', minute=30)
3: Pairs(brand='Brand X', os='XP', minute=30)
4: Pairs(brand='Brand X', os='2000', minute=60)
5: Pairs(brand='Brand Y', os='XP', minute=60)
6: Pairs(brand='Brand Y', os='98', minute=60)
7: Pairs(brand='Brand X', os='NT', minute=60)
8: Pairs(brand='Brand X', os='NT', minute=30)
9: Pairs(brand='Brand X', os='98', minute=30)
10: Pairs(brand='Brand X', os='XP', minute=15)
11: Pairs(brand='Brand X', os='2000', minute=15)
Parameterized testing pairwise by using pytest
Parameterized testing: value matrix
:Sample Code:
.. code:: python
import pytest
from allpairspy import AllPairs
def function_to_be_tested(brand, operating_system, minute) -> bool:
# do something
return True
class TestParameterized(object):
@pytest.mark.parametrize(["brand", "operating_system", "minute"], [
values for values in AllPairs([
["Brand X", "Brand Y"],
["98", "NT", "2000", "XP"],
[10, 15, 30, 60]
])
])
def test(self, brand, operating_system, minute):
assert function_to_be_tested(brand, operating_system, minute)
:Output:
.. code::
$ py.test test_parameterize.py -v
============================= test session starts ==============================
...
collected 16 items
test_parameterize.py::TestParameterized::test[Brand X-98-10] PASSED [ 6%]
test_parameterize.py::TestParameterized::test[Brand Y-NT-10] PASSED [ 12%]
test_parameterize.py::TestParameterized::test[Brand Y-2000-15] PASSED [ 18%]
test_parameterize.py::TestParameterized::test[Brand X-XP-15] PASSED [ 25%]
test_parameterize.py::TestParameterized::test[Brand X-2000-30] PASSED [ 31%]
test_parameterize.py::TestParameterized::test[Brand Y-XP-30] PASSED [ 37%]
test_parameterize.py::TestParameterized::test[Brand Y-98-60] PASSED [ 43%]
test_parameterize.py::TestParameterized::test[Brand X-NT-60] PASSED [ 50%]
test_parameterize.py::TestParameterized::test[Brand X-NT-30] PASSED [ 56%]
test_parameterize.py::TestParameterized::test[Brand X-98-30] PASSED [ 62%]
test_parameterize.py::TestParameterized::test[Brand X-XP-60] PASSED [ 68%]
test_parameterize.py::TestParameterized::test[Brand X-2000-60] PASSED [ 75%]
test_parameterize.py::TestParameterized::test[Brand X-2000-10] PASSED [ 81%]
test_parameterize.py::TestParameterized::test[Brand X-XP-10] PASSED [ 87%]
test_parameterize.py::TestParameterized::test[Brand X-98-15] PASSED [ 93%]
test_parameterize.py::TestParameterized::test[Brand X-NT-15] PASSED [100%]
Parameterized testing: OrderedDict
:Sample Code: .. code:: python
import pytest
from allpairspy import AllPairs
def function_to_be_tested(brand, operating_system, minute) -> bool:
# do something
return True
class TestParameterized(object):
@pytest.mark.parametrize(
["pair"],
[
[pair]
for pair in AllPairs(
OrderedDict(
{
"brand": ["Brand X", "Brand Y"],
"operating_system": ["98", "NT", "2000", "XP"],
"minute": [10, 15, 30, 60],
}
)
)
],
)
def test(self, pair):
assert function_to_be_tested(pair.brand, pair.operating_system, pair.minute)
Other Examples
Other examples could be found in examples <https://github.com/thombashi/allpairspy/tree/master/examples>__ directory.
Installation
Installation: pip
::
pip install allpairspy
Installation: apt
You can install the package by apt via a Personal Package Archive (PPA <https://launchpad.net/~thombashi/+archive/ubuntu/ppa>__):
::
sudo add-apt-repository ppa:thombashi/ppa
sudo apt update
sudo apt install python3-allpairspy
Known issues
-
Not optimal - there are tools that can create smaller set covering all the pairs. However, they are missing some other important features and/or do not integrate well with Python.
-
Lousy written filtering function may lead to full permutation of parameters.
-
Version 2.0 has become slower (a side-effect of introducing ability to produce n-wise combinations).
Dependencies
Python 3.7+ no external dependencies.
Sponsors
.. image:: https://avatars.githubusercontent.com/u/3658062?s=48&v=4 :target: https://github.com/b4tman :alt: Dmitry Belyaev (b4tman) .. image:: https://avatars.githubusercontent.com/u/44389260?s=48&u=6da7176e51ae2654bcfd22564772ef8a3bb22318&v=4 :target: https://github.com/chasbecker :alt: Charles Becker (chasbecker) .. image:: https://avatars.githubusercontent.com/u/46711571?s=48&u=57687c0e02d5d6e8eeaf9177f7b7af4c9f275eb5&v=4 :target: https://github.com/Arturi0 :alt: Arturi0
Become a sponsor <https://github.com/sponsors/thombashi>__
FAQs
Pairwise test combinations generator
We found that allpairspy demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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