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…
Java Type Dependency Analysis
Recently I came over a great visualization of imported classes by one class made by Mike Bostock with his Hierarchical Edge Bundling in D3. I wondered how hard it would be to reimplement this visualization with jQAssistant and Neo4j and show actual dependencies between Java types. So let’s have a look!
Visualizing Production Coverage with JaCoCo, Pandas and D3
I recently watched Michael Feathers’ talk about Strategic Code Deletion. Michael said (among other very good things) that if we want to delete code, we have to know the actual usage of our code.
In this post, I want to show you how you can very easily gather some data and create insights about unused code.
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…