Raymond Hettinger is one of the people I enjoy most giving tech talks. He is one of the people who know how to give very down to earth explanation about many complicated and fun topics. He also loves Python as I do, the difference being that he is a big contributor and bigger expert on Python. Definitely check out his talks on YouTube. In this post
What testing or debugging tools have the highest payoff for the time invested?
First, select the tooling based on the kinds of problems you have.
Here are some questions that will point you at the right direction.
- What is your error tolerance? For instance, an air-traffic control system is different than commenting logic on a blog.
- What kind of errors are typical for your application? If conflating data is a problem, static typing tools become more worthwhile.
- If you have to deal with lots of inputs, then automated input generation becomes worthwhile.
Some python tools Raymond finds really useful regarding testing:
Doctest
Doctest is good for testing documentation. It is so easy to use, you’d be a fool not to use it. It teaches you how to use your own software. Once you write a lot of software you might forget the patterns. That’s where this tool helps, it help’s you to write persuasive code (code that convinces you that it is correct).
py.test
Py.test is good for thorough testing or unit testing. Py.test will save you typing compared to Python’s
Static typing
Static typing is included in the standard library. Using it improves code clarity and helps improve documentation. It also helps write cleaner APIs. Python however does not use static types, the principal tool to check static types is mypy.
PyFlakes
Takes 5 seconds to write the line. Unlike pylint, it is not opinionated, but rather logical. Everything it tells you, is actually a bug.
Hypothesis
A library to annotate the relationships between inputs and outputs. The library then generates test cases for you.