I wrote a Jupyter notebook that demonstrates how you can easily use pandas together with pygal. Because it won’t work here on my blogging system, you have to read about it on GitHub: Jupyter notebook HTML export (fully interactive version)
Here is a short demo that I show regularly in my talks about Software Analytics. It shows how you can identify components in your code that are so old that probably nobody knows anything about it anymore.
People at conferences and meetups often ask me what I would recommend to learn X or Y. And I’m always happy to give some suggestions depending on the experience level of the person that asked. Unfortunately, this doesn’t scale very much, so here are my general recommendations on learning something very effective. This time: Python.
OK, I did it. I was guest in a podcast show. Tim Bourguignon, the host of the Developer’s Journey podcast, invited me for a recording in Nuremberg. It was a completely new experience for me being live interviewed in English for the first time.
So here we are: Me speaking about how I got into this Software Analytics thing on a podcast. Crazy 🙂 !
People at conferences and meetups often ask me what I would recommend to learn X or Y. And I’m always happy to give some suggestions depending on the experience level of the person that asked. Unfortunately, this doesn’t scale very much, so here are my general recommendations on learning something very effective. This time: pandas.
In my talks, I’m using a short example that illustrates how the mechanics of my notebook-driven approach for analyzing software systems works. Here you can now find another example on my blog, too…
2018 isn’t over yet, but especially the last six months have been an intense time for me so far. With presentations at some major conferences, traveling around to talk at several meetups and organizing some workshops as well as coding some software analyses and helping out some developers, I have to say…
In my previous blog post, we’ve seen how we can identify files that change together in one commit.
In this blog post, we take the analysis to an advanced level…
In his book Software Design X-Ray, Adam Tornhill shows a nice metric to find out if some parts of your code are coupled regarding their conjoint changes: Temporal Coupling.
In this and the next blog posts, I’m playing around with Adam’s ideas (and more) to find hidden dependencies of code parts based on version control data…
When you come across my blog, you may be thinking “What is this guy doing”? There are some weird analyses that have somehow to do with software development. But why all this?