Python library for generating and concisely specifyingreproducible pseudorandom binary data for unit testing.
Project description
Python library for generating and concisely specifying reproducible pseudorandom binary data for unit testing.
Purpose
This library makes it possible to generate pseudorandom binary test data in a reproducible way, as well as to embed concise specifications of correct function behavior on that test data. This enables the construction of functional tests within unit testing suites that fit within one-line definitions but still test a function’s behavior against a large number of inputs. More background information about this library’s purpose, design, and implementation can be found in a related article.
Package Installation and Usage
The package is available on PyPI:
python -m pip install fountains
The library can be imported in the usual ways:
import fountains from fountains import fountains
Examples
An object of the fountains class can be used to generate pseudorandom binary test data:
>>> [bs.hex() for bs in fountains(length=3, limit=4)] ['e3b0c4', 'ce1bc4', '2ed5b5', '781f5a']
Supplying a function as a parameter to a fountains object makes it possible to generate a concise (but necessarily incomplete) specification for that function’s behavior on a stream of pseudorandom inputs:
>>> add = lambda bs: bytes([(bs[0] + bs[1] + bs[2]) % 256]) >>> bits = list(fountains(3, 8, function=add)) >>> bits [0, 0, 1, 1, 1, 0, 1, 0]
When converted to a hexadecimal string, this specification encodes partial information about 4 distinct input-output test cases in every character:
>>> from bitlist import bitlist >>> bitlist(bits).hex() '3a' # Partial outputs from 8 distinct tests.
Supplying the specification generated in the manner above as an additional parameter makes it possible to test the function’s behavior:
>>> list(fountains(3, 8, function=add, bits='3a')) [True, True, True, True, True, True, True, True]
Each individual boolean value in the above represents the result of an individual test case. A different function might not satisfy the same partial specification:
>>> mul = lambda bs: bytes([(bs[0] * bs[1] * bs[2]) % 256]) >>> list(fountains(3, 8, function=mul, bits='3a')) [True, False, True, True, False, True, False, True]
Each boolean value in the outputs of the last two code blocks above may be a false negative (i.e., True may mean that the function satisfies the specification only in a portion of its output for the corresponding input) but is never a false positive signal of incorrect behavior (i.e., False indicates the function does not satisfy the specification for the corresponding input-output pair).
Documentation
The documentation can be generated automatically from the source files using Sphinx:
cd docs python -m pip install -r requirements.txt sphinx-apidoc -f -E --templatedir=_templates -o _source .. ../setup.py && make html
Testing and Conventions
All unit tests are executed and their coverage is measured when using nose (see setup.cfg for configuration details):
python -m pip install nose coverage nosetests
Alternatively, all unit tests are included in the module itself and can be executed using doctest:
python fountains/fountains.py -v
Style conventions are enforced using Pylint:
python -m pip install pylint pylint fountains
Contributions
In order to contribute to the source code, open an issue or submit a pull request on the GitHub page for this library.
Versioning
Beginning with version 0.2.0, the version number format for this library and the changes to the library associated with version number increments conform with Semantic Versioning 2.0.0.
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