Technical debt in legacy systems is difficult to communicate to management because its impact is less visible than new features or bugs.
Early software analytics aimed to create universal quality dashboards but failed because metrics and models are not transferable between unique projects.
Instead of reusing non-transferable results, teams can adapt the methodologies and tools from software analytics to answer their own unique, high-impact questions.
Reproducible data science provides the necessary methodologies and tools for open and automated analysis, leveraging skills developers already possess.
Analyzing static, runtime, chronological, and community data can reveal code ownership gaps, performance bottlenecks, and opportunities for modularization.
A live coding demo shows how to use Python, pandas, and Jupyter notebooks to analyze production code coverage data and visualize unused code packages.
Graph analytics with tools like jQAssistant and Neo4j helps visualize and query interconnected software data like class dependencies and method calls.
Successful software data analysis requires focusing on solving specific problems, working openly, automating processes, and deriving actionable next steps.
The speaker answers questions about analyzing production codebases, sharing examples of identifying performance bottlenecks and justifying technology choices with data.
29:18Markus Harrer
29:25Matthias Niehoff
50:38Radovan Kavický
41:06Lars Gentsch
30:23Luc Perard
26:53Matthias Niehoff
15:26Gregor Schumacher, Sujay Joshy & Marcel Gocke
30:40Olivier Gaudin
Jobs that call for the skills explored in this talk.
SYSKRON GmbH
Regensburg, Germany
CONTIAMO GMBH
Berlin, Germany
