The more you do programming, the more you will here about how you should test your code. You will hear about things like Extreme Programming and Test Driven Development (TDD). These are great ways to create quality code. But how does testing fit in with Jupyter? Frankly, it really doesn't. If you want to test […]
Coverage.py is a 3rd party tool for Python that is used for measuring your code coverage. It was originally created by Ned Batchelder. The term "coverage" in programming circles is typically used to describe the effectiveness of your tests and how much of your code is actually covered by tests. You can use coverage.py with […]
The unittest module now includes a mock submodule as of Python 3.3. It will allow you to replace portions of the system that you are testing with mock objects as well as make assertions about how they were used. A mock object is used for simulating system resources that aren't available in your test environment. […]
The unittest module is actually a testing framework that was originally inspired by JUnit. It currently supports test automation, the sharing of setup and shutdown code, aggregating tests into collections and the independence of tests from the reporting framework. The unittest frameworks supports the following concepts: Test Fixture - A fixture is what is used […]
Python includes a couple of modules for testing in its standard library: doctest and unittest. We will be looking at doctest in this article. The doctest module will search for pieces of text in your code that resemble interactive Python sessions. It will then execute those sessions to verify that they work exactly as written. […]
There are several code analysis tools for Python. The most well known is pylint. Then there's pychecker and now we're moving on to pyflakes. The pyflakes project is a part of something known as the Divmod Project. Pyflakes doesn't actually execute the code it checks, unlike pychecker. Of course, pylint also doesn't execute the code. […]
