Here are some queries that I use regulary at meetups and conferences for showing some features of jQAssistant and Neo4j.
jQAssistant: Cypher Query Examples
Finding tested code with jQAssistant
Introduction This blog is a three-part series. See part 2 for calculation of test similarity and part 3 (upcoming) for visualization. In an upcoming analysis, we want to calculate the structural similarity between test cases. For this, we need the
A Code Inventory for Legacy Systems
Some months ago, I did some redocumentation work of a mid-sized legacy system (~1 Mio. LOC). Among other things, the system lacked homogeneity, that means there were many terms that were differently interpreted from different teams over the time…
A graph(ical) approach towards Bounded Contexts
In this blog post, I want to show how you can get a first impression on how you can cut a monolithic application into separated components that make sense from a business’ perspective. This method can help you to identify meaningful Bounded Contexts…
Mining performance hotspots with JProfiler, jQAssistant, Neo4j and Pandas – Part 2: Root Cause Analysis
All the work before was just there to get a nice graph model that feels more natural. Now comes the analysis part: As mentioned in the introduction, we don’t only want the hotspots that signal that something awkward happened, but also
the trigger in our application of the hotspot combined with
the information about the entry point (e. g. where in our application does the problem happen) and
(optionally) the request that causes the problem (to be able to localize the problem)…
Mining performance hotspots with JProfiler, jQAssistant, Neo4j and Pandas – Part 1: The Call Graph
I show how I determine the parts of an application that trigger unnecessary SQL statements by using graph analysis of a call tree…
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 …
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” 🙂