Making good use of random in your python unit tests. Writing efficient UnitTest to validate that your code performs as expected is a difficult endeavor.
Lot has been written about the benefits of Test Driven development and on how to best approach testing, and lot can be learned reading the available litterature. One thing however that we don’t see often mentionned is that architecting efficient UnitTest is pretty hard and that no tools or testing framework are of much value without a fair understanding of the code base that needs to be tested. The techniques we will be briefly introducing now are no different. You may use them to impress your colleagues and show them TestSuite you have just written that contains millions of tests. Be aware though that increasing TestSuite test count may not be sufficient to meaningfully change code base coverage. Basic idea Assumes you wish to provide tests for an hypothetical func_to_test that looks like so : def func_to_test(x, y): ... return result Be repeatable TestCase factories. An Extended Introduction to the nose Unit Testing Framework. Welcome!
This is an introduction, with lots and lots of examples, to the nose unit test discovery & execution framework. If that's not what you want to read, I suggest you hit the Back button now. The latest version of this document can be found at (Last modified October 2006.) A unit test is an automated code-level test for a small "unit" of functionality.
(There's lots of discussion on whether unit tests should do things like access external resources, and whether or not they are still "unit" tests if they do. Unit tests are almost always pretty simple, by intent; for example, if you wanted to test an (intentionally naive) regular expression for validating the form of e-mail addresses, your test might look something like this: EMAIL_REGEXP = r'[\S.] There are a couple of ways to integrate unit tests into your development style. For me, the most important part of having unit tests is that they can be run quickly, easily, and without any thought by developers. "Why use nose in particular? " New and Improved. Coming changes to unittest in Python 2.7 & 3.2 The Pycon Testing Goat. unittest is the Python standard library testing framework.
It is sometimes known as PyUnit and has a rich heritage as part of the xUnit family of testing libraries. Python has the best testing infrastructure available of any of the major programming languages, but by virtue of being included in the standard library unittest is the most widely used Python testing framework. unittest has languished whilst other Python testing frameworks have innovated. This article will go through the major changes, like the new assert methods, test discovery and the load_tests protocol, and also explain how they can be used with earlier versions of Python.
Introduction to unittest. Starting Testing with Python Note In Python 2.7 and 3.2 a whole bunch of improvements to unittest will arrive.
The major changes include new assert methods, clean up functions, assertRaises as a context manager, new command line features, test discovery and the load_tests protocol. unittest2 is a backport of the new features (and tests) to work with Python 2.4, 2.5 & 2.6. See: unittest2: improvements to the unittest module As a dynamic language Python is substantially easier to test than other languages, which means that there is absolutely no excuse for not having good tests for your Python projects. This article is about testing in Python, mainly using the Python standard library testing framework unittest. There are many different ways of categorizing tests, and many names for subtly different styles of testing. Unit testsFunctional tests (black box tests, acceptance tests, integration tests)Regression tests Regression testing checks that bugs you’ve fixed don’t recur.
25.3. unittest — Unit testing framework — Python v2.7.2 documentation. New in version 2.1.
(If you are already familiar with the basic concepts of testing, you might want to skip to the list of assert methods.) The Python unit testing framework, sometimes referred to as “PyUnit,” is a Python language version of JUnit, by Kent Beck and Erich Gamma. JUnit is, in turn, a Java version of Kent’s Smalltalk testing framework. Unittest — Unit testing framework — Python v2.6 documentation.
New in version 2.1.
The Python unit testing framework, sometimes referred to as “PyUnit,” is a Python language version of JUnit, by Kent Beck and Erich Gamma. JUnit is, in turn, a Java version of Kent’s Smalltalk testing framework. Each is the de facto standard unit testing framework for its respective language. unittest supports test automation, sharing of setup and shutdown code for tests, aggregation of tests into collections, and independence of the tests from the reporting framework. The unittest module provides classes that make it easy to support these qualities for a set of tests. To achieve this, unittest supports some important concepts: test fixture A test fixture represents the preparation needed to perform one or more tests, and any associate cleanup actions. Test-Driven Development with Python. Test-Driven Development with Python Test-Driven Development with Python Harry Percival Gillian McGarvey Rebecca Demarest Wendy Catalano Randy Comer David Futato Copyright © 2014 Harry Percival Printed in the United States of America.
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