Markus Harrer
Data Science on Software Data
#1about 4 minutes
The challenge of justifying legacy system improvements
Technical debt in legacy systems is difficult to communicate to management because its impact is less visible than new features or bugs.
#2about 4 minutes
The promise and failure of universal software quality metrics
Early software analytics aimed to create universal quality dashboards but failed because metrics and models are not transferable between unique projects.
#3about 5 minutes
Adopting analytics approaches for project-specific questions
Instead of reusing non-transferable results, teams can adapt the methodologies and tools from software analytics to answer their own unique, high-impact questions.
#4about 5 minutes
Using data science as a foundation for software analytics
Reproducible data science provides the necessary methodologies and tools for open and automated analysis, leveraging skills developers already possess.
#5about 6 minutes
Exploring software data types and practical analysis use cases
Analyzing static, runtime, chronological, and community data can reveal code ownership gaps, performance bottlenecks, and opportunities for modularization.
#6about 13 minutes
Analyzing code coverage with Python, pandas, and Jupyter
A live coding demo shows how to use Python, pandas, and Jupyter notebooks to analyze production code coverage data and visualize unused code packages.
#7about 3 minutes
An introduction to graph analytics for software systems
Graph analytics with tools like jQAssistant and Neo4j helps visualize and query interconnected software data like class dependencies and method calls.
#8about 1 minute
Key principles for effective software data analysis
Successful software data analysis requires focusing on solving specific problems, working openly, automating processes, and deriving actionable next steps.
#9about 8 minutes
Q&A on production code analysis and performance bottlenecks
The speaker answers questions about analyzing production codebases, sharing examples of identifying performance bottlenecks and justifying technology choices with data.
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