![Session Summary [DevOps Camp compact 2017]](https://www.feststelltaste.de/wp-content/uploads/2017/10/game_over-270x225.jpg "Session Summary [DevOps Camp compact 2017]")
At the BarCamp “DevOps Camp compact 2017” last weekend, I moderated a discussion round based on the question
“What prevents you from doing software development effectively?”
The original reason for this question was that I wanted to know…
When working with bigger software systems, it’s easy to get lost in all the source code that makes up the system. A good software system has to provide a structure that allows developers to quickly grasp the main ideas of a system. A proven method to achieve this is using hierarchies and schemas. With this approach, tiny things can be summed up to bigger ones that make somehow sense in a more broader view…
In Carola Lilienthal’s talk about architecture and technical debt at Herbstcampus 2017, I was reminded that I wanted to implement some of the examples of her book “Long-lived software systems” (available only in German) with the structural analysis tool jQAssistant. Especially the visualizations of the dependencies between different business subdomains seemed like a great starting point to try out some stuff…
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!
In software development, it’s all about knowledge – both technical and the business domain. But we software developers transfer only a small part of this knowledge into code. But code alone isn’t enough to get a glimpse of the greater picture and the interrelations of all the different concepts. There will be always developers that know more about some concept as laid down in source code. It’s important to make sure that this knowledge is distributed over more than one head…
There are multiple reasons for analyzing a version control system like your Git repository. See for example Adam Tornhill’s book “Your Code as a Crime Scene” or his upcoming book “Software Design X-Rays” for plenty of inspirations:
You can analyze knowledge islands, distinguish often changing code from stable code parts, identify code that is temporal coupled to other code.
Having the necessary data for those analyses in a Pandas DataFrame gives you many possibilities to quickly gain insights into the evolution of your software system in various ways…
Maybe you’re reading my blog for some time. Maybe you just came over for the
first visit to my blog. You might be browsing through some content. And there it
comes, the inevitable question: “What is this guy doing here?”…
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.
I gave a talk at Java Forum Stuttgart 2017 on July 5th 2017. I was a little bit nervous because it was my first Pecha Kucha talk and a very tricky topic at the same time: Epistemology and its influence
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)…