{ "info": { "author": "Alexander Plavin", "author_email": "alexander@plav.in", "bugtrack_url": null, "classifiers": [ "Development Status :: 4 - Beta", "Intended Audience :: Developers", "License :: OSI Approved :: MIT License", "Operating System :: OS Independent", "Programming Language :: Python", "Programming Language :: Python :: 2", "Programming Language :: Python :: 2.7", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.3", "Programming Language :: Python :: Implementation :: CPython", "Programming Language :: Python :: Implementation :: PyPy", "Topic :: Software Development", "Topic :: Software Development :: Libraries", "Topic :: Software Development :: Libraries :: Python Modules", "Topic :: Software Development :: Testing" ], "description": "`pyquchk` - random and serial testing\r\n==========================================\r\n\r\n`pyquchk` (abbreviated from Python QuickCheck) is the most comprehensive\r\nand extensible, altrough easy-to-use, random and serial testing framework.\r\nAs you see from the name, it's inspired by\r\n`Haskell QuickCheck `_,\r\nbut aims to provide much more capabilities.\r\n\r\nFull documentation, including usage examples, is available at\r\n`pythonhosted.org/pyquchk `_.\r\n\r\nFeatures\r\n========\r\n\r\nAlready implemented\r\n^^^^^^^^^^^^^^^^^^^\r\n\r\n`pyquchk` combines two approaches to the arguments values generation in a\r\nsingle framework:\r\n\r\n* `QuickCheck `_-like (random)\r\n\r\n Random (fuzzy) testing runs a function for many randomly generated arguments\r\n values to make sure it's generally correct. 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