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…
Wie können andere sagen, worauf es Dir ankommt?
Bei der Entwicklung von Software könnten unsere Gefühle – wenn wir hier überhaupt etwas fühlen – von Furcht oder Schmerz dominiert werden, die durch Zeitdruck, komplexen Probleme, unklaren Lösungen, beurteilenden Teamkollegen / Managern / Kunden, verpassten Terminen oder Bugs entstehen. Es braucht Geduld und Disziplin, aber wir können es hinbekommen, uns anders zu fühlen…
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 …