In the past, I did a lot of Git log analysis on my blog. The main reason is that developers know what Git is and what kind of data it provides. So it is easy to connect to developers then
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…
Here is a short video that demonstrates how you can get some insights from the history of a Git repository using Jupyter Notebook, Python, pandas and matplotlib: We take a look at exporting the necessary data reading in the dataset
In this blog post, I want to show you a nice complexity metric that works for most major programming languages that we use for our software systems – the indentation-based complexity metric.
This notebook is a simple mini-tutorial to introduce you to basic functions of Jupyter, Python, Pandas and matplotlib with the aim of analyzing software data. Therefore, the example is chosen in such a way that we come across the typical methods in a data analysis.