Introduction Software architects have to make sure that the communicated software architecture blueprints exist in the real world. For this, manual inspections as well as automated measurements are needed to avoid surprises. In this notebook, I want to show how
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.
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…
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…