 |
 |
 |
Python Library Reference |  |
 |
 |
|
|
|
Python Library Reference | |
|
|
As mentioned in the introduction, doctest has grown to have three primary uses:
These uses have different requirements, and it is important to distinguish them. In particular, filling your docstrings with obscure test cases makes for bad documentation.
When writing a docstring, choose docstring examples with care. There's an art to this that needs to be learned--it may not be natural at first. Examples should add genuine value to the documentation. A good example can often be worth many words. If done with care, the examples will be invaluable for your users, and will pay back the time it takes to collect them many times over as the years go by and things change. I'm still amazed at how often one of my doctest examples stops working after a "harmless" change.
Doctest also makes an excellent tool for regression testing, especially if you don't skimp on explanatory text. By interleaving prose and examples, it becomes much easier to keep track of what's actually being tested, and why. When a test fails, good prose can make it much easier to figure out what the problem is, and how it should be fixed. It's true that you could write extensive comments in code-based testing, but few programmers do. Many have found that using doctest approaches instead leads to much clearer tests. Perhaps this is simply because doctest makes writing prose a little easier than writing code, while writing comments in code is a little harder. I think it goes deeper than just that: the natural attitude when writing a doctest-based test is that you want to explain the fine points of your software, and illustrate them with examples. This in turn naturally leads to test files that start with the simplest features, and logically progress to complications and edge cases. A coherent narrative is the result, instead of a collection of isolated functions that test isolated bits of functionality seemingly at random. It's a different attitude, and produces different results, blurring the distinction between testing and explaining.
Regression testing is best confined to dedicated objects or files. There are several options for organizing tests:
_regrtest_topic that
consist of single docstrings, containing test cases for the
named topics. These functions can be included in the same file
as the module, or separated out into a separate test file.
__test__ dictionary mapping from regression test
topics to docstrings containing test cases.
|
|
|
Python Library Reference | |
|
|