allpairspy forked from bayandin/allpairs

• AllPairs test combinations generator
• Features
• Get Started
  • Basic Usage
  • Filtering
  • Data Source: OrderedDict
  • Parameterized testing with pairwise by using pytest
  • Other Examples
• Installation
  • Installation: pip
  • Installation: apt
• Known issues
• Dependencies
• Sponsors

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:

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:

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 filtering function to filter_func at AllPairs constructor.

Sample Code:

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:

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:

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:

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 with pairwise by using pytest

Parameterized testing: valee matrix

Sample Code:

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:

$ 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:

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 directory.

Installation

Installation: pip

pip install allpairspy

Installation: apt

You can install the package by apt via a Personal Package Archive (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 3.5+ no external dependencies.

Sponsors

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