This guide is the first in the Unit Testing for Scale and Profit series. In a project where changes are frequently made to research software, it is helpful to know that the code still works as expected. In our last two episodes, we looked at the benefits of having a set of unit tests and how we can use test parameterisation to write numerous tests efficiently. However, particularly with projects involving more than one contributor, it would be good to have assurance the software still works without everyone having to pull down all the changes and test them. In this guide, we'll be looking at…
This guide is the second in the Unit Testing for Scale and Profit series. As our code increases in size and particularly complexity, we should expect our number of tests to increase too, which means more time writing tests. Fortunately there is something that can help with this burden which we'll look at in this guide: parameterised tests!
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This guide is the first in the Unit Testing for Scale and Profit series. In this guide we’ll look into techniques of automated testing to improve the predictability of a software change, make development more productive, and help us produce code that works as expected and yields desired results. We'll use Python for illustration purposes, but the concepts and approaches can be readily applied to many other languages.