Section 3: Automatizing code quality checks

Overview

Teaching: 5 min
Exercises: 0 min

Questions

• How to set up automatic testing for all contributors of the project?

• How to enforce coding style conventions?

• How to estimate computational effectiveness of the software and find bottlenecks?

Objectives

• Introduce the tools, techniques, and best practices for automatizing your work on ensuring the quality of the software.

After learning what testing is and how to write tests manually, we will learn how to make sure that you do not skip the testing fase in a hurry. We also cover how to profile the time and computational resources your software requires.

In this section we will:

• Automatically run a set of unit tests using GitHub Actions - a Continuous Integration infrastructure that allows us to automate tasks when things happen to our code, such as running those tests when a new commit is made to a code repository.
• Use Pylint to check code style conventions.
• Use Jupyter magic commands to measure performance time of the code.
• Use library SnakeViz to profile resources taken by the software.

Key Points

• Running tests every time when the code is updates is tiresome, and we can be tempted to skip this step. We should use automatic Continuous Integration tools, such as GitHub Actions, to make sure that the tests are executed regularly.

• Continuous Integration also allows us to set up an automatic code style checks.

• To check how efficient our code is and find bottlenecks, we can use Jupyter Lab built-in magic commands and Python libraries, such as cProfile and SnakeViz