Guest post by Stefan Feuerriegel – originally published here.
Software testing describes several means to investigate program code regarding its quality. The underlying approaches provides means to handle errors once they occur. Furthermore, software testing also show techniques to reduce the probability of that.
R is becoming a increasingly promiment programming language. This not only includes pure statistical settings but also machine learning, dashboards via Shiny and beyond. This development is simulateneously fueled by the business schools teaching R to their students. While software testing is usually covered from a theoretical viewpoint, our slides teach the basics on software testing in an easy-to-understand fashion with the help of R.
Our slide deck aims at bridging R programming and software testing. The slides outline the need for software testing and describe general approaches, such as the V model. In addition, we present the build-in features for error handling in R and also show how to do unit testing with the help of the “testthat” package.
We hope that the slide deck supports practitioners to unleash the power of unit testing in R. Moreover, it should equip scholars in business schools with knowledge on software testing.