.. contents:: allpairspy forked from bayandin/allpairs <https://github.com/bayandin/allpairs>__
:backlinks: top
:depth: 2

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:alt: PyPI package version

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:target: https://pypi.org/project/allpairspy
:alt: Supported Python versions

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: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>__

Dependencies of allpairspy (2).
Dependency	Optional
pytest	true
pytest-md-report	true

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allpairspy

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