I show how I determine the parts of an application that trigger unnecessary SQL statements by using graph analysis of a call tree…
A simple demo on how to use Python Pandas with jQAssistant / Neo4j
I’m a huge fan of the software analysis framework jQAssistant. It’s a great tool for scanning and validating various software artifacts. But I also love Python Pandas as a powerful tool in combination with Jupyter notebook for reproducible Software Analytics.
Combining these tools is near at hand. So I’ve created a quick demonstration for “first contact” 🙂