fountains 2.2.0

Python library for generating and concisely specifying reproducible pseudorandom binary data for unit testing.

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.

Installation and Usage

This library is available as a package 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 four distinct input-output test cases in every character:

>>> from bitlist import bitlist
>>> bitlist(bits).hex()
'3a' # Partial outputs from eight 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).

Development

All installation and development dependencies are fully specified in pyproject.toml. The project.optional-dependencies object is used to specify optional requirements for various development tasks. This makes it possible to specify additional options (such as docs, lint, and so on) when performing installation using pip:

python -m pip install .[docs,lint]

Documentation

The documentation can be generated automatically from the source files using Sphinx:

python -m pip install .[docs]
cd docs
sphinx-apidoc -f -E --templatedir=_templates -o _source .. && make html

Testing and Conventions

All unit tests are executed and their coverage is measured when using pytest (see the pyproject.toml file for configuration details):

python -m pip install .[test]
python -m pytest

Alternatively, all unit tests are included in the module itself and can be executed using doctest:

python src/fountains/fountains.py -v

Style conventions are enforced using Pylint:

python -m pip install .[lint]
python -m pylint src/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.

Publishing

This library can be published as a package on PyPI by a package maintainer. First, install the dependencies required for packaging and publishing:

python -m pip install .[publish]

Ensure that the correct version number appears in pyproject.toml, and that any links in this README document to the Read the Docs documentation of this package (or its dependencies) have appropriate version numbers. Also ensure that the Read the Docs project for this library has an automation rule that activates and sets as the default all tagged versions. Create and push a tag for this version (replacing ?.?.? with the version number):

git tag ?.?.?
git push origin ?.?.?

Remove any old build/distribution files. Then, package the source into a distribution archive:

rm -rf build dist src/*.egg-info
python -m build --sdist --wheel .

Finally, upload the package distribution archive to PyPI:

python -m twine upload dist/*