I show how I determine the parts of an application that trigger unnecessary SQL statements by using graph analysis of a call tree…
Mining performance hotspots with JProfiler, jQAssistant, Neo4j and Pandas – Part 1: The Call Graph
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I show how I determine the parts of an application that trigger unnecessary SQL statements by using graph analysis of a call tree…
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” 🙂