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World Congress 2021 • Jul 30, 2021

Data Science on Software Data

Markus Harrer

Struggling to convince management to fix technical debt? Discover how to use Python and Jupyter to turn version control history into data-driven arguments for structural investments.

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#1 about 2 min

Analyzing legacy systems using software data

Applying data analysis approaches to extract insights and improve decisions regarding legacy software architecture.

#2 about 3 min

Competing priorities between technical improvements and feature development

Competing business priorities cause necessary architectural updates to struggle for budget against highly visible feature requests and defect resolution.

#3 about 6 min

Transferability challenges in standard software analytics models

Early empirical software engineering research demonstrated the inability to transfer quality metrics across different unique IT projects.

#4 about 5 min

Choosing software analytics over standard static analysis tools

Moving beyond standard style checkers involves building specific analytical views that address unique project problems.

#5 about 2 min

Baseline developer skills for software data science

Standard software developers already possess the required quantitative, programming, and domain expertise to perform data science.

#6 about 3 min

Establishing reproducible data science with openness and automation

Integrating data tools into the continuous delivery pipeline achieves transparent and automated code analysis.

#7 about 6 min

Extracting insights from various types of software data

Mining static, runtime, chronological, and community development data exposes abandoned code and structural hotspots.

#8 about 6 min

Building a data analysis stack with Python and Jupyter

Utilizing Python, pandas, and computational notebooks documents and executes analytical assumptions transparently.

#9 about 7 min

Analyzing production code coverage data using pandas

Reading Jacoco execution traces into data frames visualizes unused application packages via grouped bar charts.

#10 about 3 min

Utilizing graph analytics and Neo4j for structural dependencies

Querying an interconnected code base modeled inside a graph database identifies code smells and architectural bounds.

#11 about 2 min

Core takeaways for adopting software data analytics workflows

Effective data analysis relies on custom tooling and verifiable, action-driven results rather than generating unapplied metrics.

#12 about 8 min

Resolving real-world performance bottlenecks through targeted data analysis

Custom dependency graphing isolated the exact Java object duplications that choked application scalability in production.

Matching moments

56 sec

Introduction to analytical data formats for software developers

Matthias Niehoff Matthias Niehoff · WWC Europe 2026

3:40 min

Identifying core challenges in modern software development

Markus Eisele Markus Eisele · WWC 2024

3:27 min

Transitioning away from dominant management with objective analytics

Laura Möller Laura Möller +3 · WWC 2024

5:19 min

Unifying core software principles for better team performance

Richard Bown · WWC 2023

5:39 min

Managing legacy infrastructure and distributed software systems

Daniel Geisel Daniel Geisel +1 · WWC Europe 2026

6:14 min

Historical evolution of software delivery and agile methodologies

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