Testing

Recently, I struggled for a couple of hours to understand why the API tests of one project were slow. In theory, we designed tests to run in a fully parallel way – the duration of tests should be close to the longest-running test. Unfortunately, the reality was different. Tests took 7x longer than the slowest test without using 100% available resources.

This post is a direct follow-up to Microservices test architecture where I’ve introduced new kinds of tests to our example project. Wild Workouts uses Google Cloud Build as CI/CD platform. It’s configured in a continuous deployment manner, meaning the changes land on production as soon as the pipeline passes. If you consider our current setup, it’s both brave and naive. We have no tests running there that could save us from obvious mistakes (the not-so-obvious mistakes can rarely be caught by tests, anyway).

Do you know the rare feeling when you develop a new application from scratch and can cover all lines with proper tests? I said “rare” because most of the time, you will work with software with a long history, multiple contributors, and not so obvious testing approach. Even if the code uses good patterns, the test suite doesn’t always follow.

Did you ever hear about a project where changes were tested on customers that you don’t like or countries that are not profitable? Or even worse – did you work on such project? It’s not enough to say that it’s just not fair and not professional. It’s also hard to develop anything new because you are afraid to make any change in your codebase.