Testing Your Code — The Hitchhiker's Guide to Python (original) (raw)

Testing your code is very important.

Getting used to writing testing code and running this code in parallel is now considered a good habit. Used wisely, this method helps to define your code’s intent more precisely and have a more decoupled architecture.

Some general rules of testing:

• A testing unit should focus on one tiny bit of functionality and prove it correct.
• Each test unit must be fully independent. Each test must be able to run alone, and also within the test suite, regardless of the order that they are called. The implication of this rule is that each test must be loaded with a fresh dataset and may have to do some cleanup afterwards. This is usually handled by setUp() and tearDown() methods.
• Try hard to make tests that run fast. If one single test needs more than a few milliseconds to run, development will be slowed down or the tests will not be run as often as is desirable. In some cases, tests can’t be fast because they need a complex data structure to work on, and this data structure must be loaded every time the test runs. Keep these heavier tests in a separate test suite that is run by some scheduled task, and run all other tests as often as needed.
• Learn your tools and learn how to run a single test or a test case. Then, when developing a function inside a module, run this function’s tests frequently, ideally automatically when you save the code.
• Always run the full test suite before a coding session, and run it again after. This will give you more confidence that you did not break anything in the rest of the code.
• It is a good idea to implement a hook that runs all tests before pushing code to a shared repository.
• If you are in the middle of a development session and have to interrupt your work, it is a good idea to write a broken unit test about what you want to develop next. When coming back to work, you will have a pointer to where you were and get back on track faster.
• The first step when you are debugging your code is to write a new test pinpointing the bug. While it is not always possible to do, those bug catching tests are among the most valuable pieces of code in your project.
• Use long and descriptive names for testing functions. The style guide here is slightly different than that of running code, where short names are often preferred. The reason is testing functions are never called explicitly.square() or even sqr() is ok in running code, but in testing code you would have names such as test_square_of_number_2(),test_square_negative_number(). These function names are displayed when a test fails, and should be as descriptive as possible.
• When something goes wrong or has to be changed, and if your code has a good set of tests, you or other maintainers will rely largely on the testing suite to fix the problem or modify a given behavior. Therefore the testing code will be read as much as or even more than the running code. A unit test whose purpose is unclear is not very helpful in this case.
• Another use of the testing code is as an introduction to new developers. When someone will have to work on the code base, running and reading the related testing code is often the best thing that they can do to start. They will or should discover the hot spots, where most difficulties arise, and the corner cases. If they have to add some functionality, the first step should be to add a test to ensure that the new functionality is not already a working path that has not been plugged into the interface.

The Basics¶

unittest¶

unittest is the batteries-included test module in the Python standard library. Its API will be familiar to anyone who has used any of the JUnit/nUnit/CppUnit series of tools.

Creating test cases is accomplished by subclassing unittest.TestCase.

import unittest

def fun(x): return x + 1

class MyTest(unittest.TestCase): def test(self): self.assertEqual(fun(3), 4)

As of Python 2.7 unittest also includes its own test discovery mechanisms.

Doctest¶

The doctest module searches for pieces of text that look like interactive Python sessions in docstrings, and then executes those sessions to verify that they work exactly as shown.

Doctests have a different use case than proper unit tests: they are usually less detailed and don’t catch special cases or obscure regression bugs. They are useful as an expressive documentation of the main use cases of a module and its components. However, doctests should run automatically each time the full test suite runs.

A simple doctest in a function:

def square(x): """Return the square of x.

>>> square(2)
4
>>> square(-2)
4
"""

return x * x

if name == 'main': import doctest doctest.testmod()

When running this module from the command line as in python module.py, the doctests will run and complain if anything is not behaving as described in the docstrings.