allpairspy forked from bayandin/allpairs
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.
- 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.
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 X', 'XP', 'Internal', 'Part-Time', 6]
12: ['Brand X', '2000', 'Internal', 'Hourly', 30]
13: ['Brand Y', '98', 'Modem', 'Contr.', 15]
14: ['Brand Y', 'XP', 'Modem', 'Salaried', 10]
15: ['Brand X', 'NT', 'Internal', 'Part-Time', 15]
16: ['Brand X', 'XP', 'Internal', 'Part-Time', 30]
17: ['Brand Y', '98', 'Modem', 'Part-Time', 60]
18: ['Brand Y', '2000', 'Modem', 'Salaried', 6]
19: ['Brand X', 'NT', 'Internal', 'Contr.', 10]
20: ['Brand X', '98', 'Internal', 'Hourly', 10]
21: ['Brand Y', 'XP', 'Modem', 'Contr.', 6]
22: ['Brand Y', 'XP', 'Modem', 'Salaried', 60]
23: ['Brand X', 'NT', 'Internal', 'Salaried', 15]
|
You can restrict pairs by setting a 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 X', '98', 'Internal', 'Contr.', 60]
12: ['Brand X', '2000', 'Internal', 'Salaried', 15]
13: ['Brand Y', 'NT', 'Modem', 'Salaried', 10]
14: ['Brand Y', 'XP', 'Modem', 'Part-Time', 6]
15: ['Brand X', '98', 'Internal', 'Hourly', 30]
16: ['Brand X', '2000', 'Internal', 'Contr.', 60]
17: ['Brand Y', 'NT', 'Modem', 'Hourly', 15]
18: ['Brand X', '98', 'Internal', 'Part-Time', 10]
19: ['Brand Y', 'XP', 'Modem', 'Salaried', 10]
20: ['Brand X', '2000', 'Internal', 'Salaried', 6]
21: ['Brand Y', 'NT', 'Modem', 'Part-Time', 60]
22: ['Brand X', '98', 'Internal', 'Hourly', 60]
23: ['Brand Y', 'XP', 'Modem', 'Salaried', 30]
|
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=30)
8: Pairs(brand='Brand X', os='NT', minute=60)
9: Pairs(brand='Brand Y', os='98', minute=30)
10: Pairs(brand='Brand Y', os='XP', minute=15)
11: Pairs(brand='Brand X', os='2000', minute=15)
|
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 Y-98-15] PASSED [ 62%]
test_parameterize.py::TestParameterized::test[Brand Y-XP-60] PASSED [ 68%]
test_parameterize.py::TestParameterized::test[Brand X-2000-10] PASSED [ 75%]
test_parameterize.py::TestParameterized::test[Brand X-2000-60] PASSED [ 81%]
test_parameterize.py::TestParameterized::test[Brand Y-XP-10] PASSED [ 87%]
test_parameterize.py::TestParameterized::test[Brand Y-98-30] PASSED [ 93%]
test_parameterize.py::TestParameterized::test[Brand X-NT-15] PASSED [100%]
|
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 could be found in examples directory.
pip install allpairspy
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
- 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).
Python 3.7+ no external dependencies.