Introduction 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
I gave a talk at Java Forum Stuttgart 2017 on July 5th 2017. I was pretty nervous because it was my first Pecha Kucha talk and a very tricky topic at the same time: Epistemology and its influence on software
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…
I’ve been wondering why we do the same errors in software development over and over again and are expecting different results. It seems to me that we can’t see the fundamental issues that come with software development and thus aren’t learning that much in our guild. We are creating software professionally for half a century but still fail at completing projects on time, on budget and with suitable quality.
Michael Feathers describes how to safely remove code aligned with the business’ needs. Here is what I took away…
You all know word clouds!
They give you a quick overview of the top topics of your blog, book, source code – or presentation. The latter was the one that got me thinking: How cool would it be if you start your presentation with a word cloud of the main topics of your talk…
Reading data from a software version control system can be pretty useful if you want to answer some evolutionary questions like
– Who are our main committers to the software?
– Are there any areas in the code where only one developer knows of?
– Where were we working on the last months?
Software version control systems contain a huge amount of evolutionary data. It’s very common to mine these repositories to gain some insight about how the development of a software product works. But there is the need for some preprocessing of that data to avoid false analysis.
That’s why I show you how to read the commit information of a Git repository into Pandas’ DataFrame!
In preparation for a talk about performance optimization, I needed some monstrous amounts of fake data for a system under test. I choose the Spring Pet Clinic project as my “patient” because there are some typical problems that this application does wrong. But this application comes with round about 100 database entries. This isn’t enough at all…