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| Check out coverage.py, at http://www.garethrees.org. This module runs your code, then produces a report on how many statements were executed, and which ones were not. Use it to ensure your unit tests test everything. | * UnitTest in the standard library (http://docs.python.org/lib/module-unittest.html) * PyUnit at http://pyunit.sourceforge.net * [[http://www.garethrees.org/2001/12/04/python-coverage/|StatementCoverage]] This module runs your code, then produces a report on how many statements were executed, and which ones were not. Use it to ensure your unit tests test everything. * DataTest at http://formencode.org/docs/DataTest/README.html * McCabe-like Python Cyclomatic Complexity analysis tools are available at http://journyx.com/curt/complexity.html. They're written in Perl, but read and analyze only Python code. Complexity is bad, this will help you simplify code - especially code you didn't write. * [[http://www.python.org/pypi/zope.testing|zope.testing]] provides a powerful test runner that supports test discovery and a wide range of options to control how tests are run and results reported. * [[http://somethingaboutorange.com/mrl/projects/nose/|nose]] is "a discovery-based [[unittest]] [[extension]]" that generally also supports PyTest functionality. == Best Practices == == Discussion == Sprouted out by http://formencode.org/docs/DataTest/TODO.html. What I need is a layered test system like * test suit * with fast/normal/detailed mode * with known failing tests excluded * test package to group related tests with preset parameters * individual test * with options like log details * with ability to flex parameters, extend options etc. * test utilities * fuzzy difference * different logging/reporting/visualization helpers * with output capture capability -- MikeRovner <<DateTime(2004-02-27T19:25:32Z)>> |
Software
UnitTest in the standard library (http://docs.python.org/lib/module-unittest.html)
StatementCoverage This module runs your code, then produces a report on how many statements were executed, and which ones were not. Use it to ensure your unit tests test everything.
McCabe-like Python Cyclomatic Complexity analysis tools are available at http://journyx.com/curt/complexity.html. They're written in Perl, but read and analyze only Python code. Complexity is bad, this will help you simplify code - especially code you didn't write.
zope.testing provides a powerful test runner that supports test discovery and a wide range of options to control how tests are run and results reported.
nose is "a discovery-based unittest extension" that generally also supports PyTest functionality.
Best Practices
Discussion
Sprouted out by http://formencode.org/docs/DataTest/TODO.html.
What I need is a layered test system like
- test suit
- with fast/normal/detailed mode
- with known failing tests excluded
- test package to group related tests with preset parameters
- individual test
- with options like log details
- with ability to flex parameters, extend options etc.
- test utilities
- fuzzy difference
- different logging/reporting/visualization helpers
- with output capture capability
-- MikeRovner 2004-02-27 19:25:32