In my previous blog post, we’ve seen how we can identify files that change together in one commit.
In this blog post, we take the analysis to an advanced level…
Here are some queries that I use regulary at meetups and conferences for showing some features of jQAssistant and Neo4j.
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
In my talks, I’m using a short example that illustrates how the mechanics of my notebook-driven approach for analyzing software systems works. Now you can find the example on my blog, too…
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…
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…
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!
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.
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)…
I show how I determine the parts of an application that trigger unnecessary SQL statements by using graph analysis of a call tree…