Each step in the development or use of software leaves valuable, digital tracks. The analysis of this "software data" (such as runtime measures, log files or commits) refines our gut feeling to facts with sound evidence.
I'll show how questions that arise in software development can be answered automated, data-driven and reproducible. I demonstrate the interaction of open source analysis tools (such as jQAssistant, Neo4j, Pandas, and Jupyter) for the analysis of data from different sources (such as JProfiler, Jenkins, and Git). Together, we have a look at how we can develop solutions to optimize performance, identify build breaker or make knowledge gaps in our source code visible.