The importance of unit testing & version control for scientific software

This post is inspired by the fact that I’m teaching software carpentry in Denmark this week, but I have had this conversation a few times, so I thought I should write it down.

Often the reaction to teaching things like version control or unit testing to scientists is of the sort aren’t these things more appropriate for professional software developers who can put in the effort to learn them? I strongly disagree.

In fact, I’ll defend that unit testing and version control are more important for science than commercial software.


Let’s say you are running a web-based business. Unfortunately, your website’s code is a mess. Many of features were implemented by someone who left a while back and none of your new hires really understands what that code does. Fortunately, however, the code works, the site is pleasing to the eye and customers are happily paying for your services. Even these old code bases can have their lives stretched for far longer than you’d expect. Life is not that bad.

Let’s say, on the other hand, you are running a computer-based science enterprise. Unfortunately, your code is a mess. Many of the features were implemented by someone who left a while back and none of your new hires really understands what that code does. Fortunately, the code produces pretty plots. Unfortunately, you cannot explain what the plots represent beyond a vague idea. You can adapt the code to a new dataset, but never really sure why it’s working like it is and sometimes the outputs are downright mysterious. Life is pretty bad. You need to start over.


The difference is that in many commercial aspects, only the final output matters. If a website is pretty, it won’t much matter whether the CSS behind it is a mess. If the search engine gives the customers want they want, both costumer and vendor are happy and nobody will say I’ll buy, but first can we go over the methodological details? There are solid reasons to make the code clean and well-tested (in terms of minimizing the negative impact of individual members leaving the team or avoiding increasing costs to maintenance & extension), but it’s not required for success.

In science, however, it is not enough to have a pretty output plot. You also need to be able to explain the details behind the plot and be certain that the plot was produced the way you think it was produced. If the code gives a mysterious result, then it’s not OK to just add a hacky line of code adding 10 to result to make it work. Similarly, the ability to go back in time in your code is a nice thing in business, but can be essential in science because we value reproducibility.


This is why I think it’s very good that unit testing & version control are both part of the software carpentry core curriculum.

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