We all know it is important to write clean, well-documented, tested code, but how do you ensure your code meets those standards?

At Lincoln Loop, we generate a lot of code and need a way to automate the bulk of our code quality checks. A continuous integration server and a handful of other tools help us keep our code quality in check.

This talk will be an overview of the following tools and provide examples of getting them to work together:

• Hudson: continuous integration server
• Pylint: analyzes Python source code looking for bugs and signs of poor quality
• Nose: extends unittest to make testing easier
• Coverage.py: a tool for measuring code coverage of Python programs