I recently published a major update for the Python tmtoolkit package for text mining and topic modeling. Since it is a fairly large research software package, I’m using a Continuous Integration (CI) system for automated testing on different platforms. This system makes sure that every code update that is pushed to the software repository is automatically checked by running the test suite on all three major operating systems (Linux, MacOS, Windows). For the recent update of tmtoolkit, I decided to move the CI system from Travis CI to GitHub Actions (GHA) since GHA is directly integrated into GitHub and easy to set up. Still, there are some obstacles to overcome so this short post shows how to set up GHA for a Python project with a few extra requirements such as installing system packages on the test runner machine or running tests with tox and hypothesis.
Read More →Developing a complex R Shiny app – the good, the bad and the ugly
Together with Clara Bicalho (UC Berkeley) and Sisi Huang (WZB), I recently developed a web application that acts as a convenient interface to the DeclareDesign R package and its repository of research designs, DesignLibrary. This web application, which we called DeclareDesign Wizard, allows users to investigate and customize research designs in their web browser. We used R Shiny for implementing it and since this was my first large Shiny project, I wanted to reflect a bit on the development process and tell in which parts Shiny shone, and in which it didn’t.
Read More →Property based testing for scientific code in Python
Automated software testing starts with the often annoying and time-consuming process of writing tests. But no matter how annoying it is, in the end it always pays off, at least that’s my experience. For this article, I assume that the reader acknowledges the importance of automated software testing, because I would like to point to a way on how to write better tests in less time by using property based testing.
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