Testing Code
Created by Chia, Jonathan on Apr 09, 2022
Introduction
You already test your code manually, so why not write a few more lines of code to document and automate your tests?
Benefits of Unit Testing
Detect bugs earlier: Running big data projects is time consuming. You don't want to get an unexpected output after 3-hours when you could have easily avoided it.
Easier to update code: You will no longer be afraid of changing your code because you know what to expect.
Push you to organize your code: You will write cleaner code and prefer to write in DAGs instead of linearly chaining functions when you keep in mind you are gonna test your codes with isolated pieces. (use d6tflow to build data science workflows easily)
Give you confidence on the outputs: Bad data leads to bad decisions. Running unit tests gives you confidence on data quality. You know your code outputs what you want it to output.
- Norman Niemer, Chief Data Scientist
Useful Links:
R
Shiny
https://mastering-shiny.org/scaling-testing.html
Python
https://towardsdatascience.com/unit-testing-for-data-scientists-dc5e0cd397fb
Unit Testing with SQL Developer 21.2 (PL/SQL)
https://docs.oracle.com/cd/E15846_01/doc.21/e15222/unit_testing.htm#RPTUG45000
Document generated by Confluence on Apr 09, 2022 16:54
Last updated