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…
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 …
Eines der agilen Prinzipien lautet: “Funktionierende Software mehr als umfassende Dokumentation”. Viele Entwickler sind daher der Meinung, dass sie überhaupt nichts mehr dokumentieren bzw. im Code kommentieren müssen. Wer im Manifest jedoch weiterliest, erfährt “obwohl wir die Werte auf der rechten Seite wichtig finden, schätzen wir die Werte auf der linken Seite höher ein”. Dokumentation spielt auch in der agilen Softwareentwicklung sehr wohl eine wichtige Rolle…
…Understanding the Memory Usage of Your Application Notes With 32 bit architectures, you’ll quickly run out of memory. java.lang.Integer is 4 times bigger than an 32-bit int and 9 times bigger than an 64-bit int. CompressedOpps reduces this overhead
Lately, I came across an old presentation of Adam Bien about Vernunft-Driven-Design (in English: Rationality- or Reason-Driven-Design). I generally like the mindset of Adam Bien and I found it particularly interesting that he talks about non-technical stuff in this video.