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
Word Cloud Computing
You all know word clouds!
They give you a quick overview of the top topics of your blog, book, source code – or presentation. The latter was the one that got me thinking: How cool would it be if you start your presentation with a word cloud of the main topics of your talk…
Reading a Git log file output with Pandas
Reading data from a software version control system can be pretty useful if you want to answer some evolutionary questions like
– Who are our main committers to the software?
– Are there any areas in the code where only one developer knows of?
– Where were we working on the last months?
Storing Git commit information into Pandas’ DataFrame
Software version control systems contain a huge amount of evolutionary data. It’s very common to mine these repositories to gain some insight about how the development of a software product works. But there is the need for some preprocessing of that data to avoid false analysis.
That’s why I show you how to read the commit information of a Git repository into Pandas’ DataFrame!
My Experiences with jQAssistant so far
jQAssistant (http://www.jqassistant.org) is a great tool for scanning and validating software structures (like Maven projects, Java bytecode or Git repositories). It also supports documenting your architectural rules and design conventions in a central place. I think jQAssistant is one of the most awesome tools out there …