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Software Analytics with Data Science on Softwar...

Software Analytics with Data Science on Software Data

Data Science has demonstrated its value in extracting insights from business data, raising the question: Why not apply these principles to our software systems’ data? In this talk, I’ll introduce you to the world of Software Analytics. We’ll explore how to extract valuable insights from software data by using tools and techniques from data science to get rid of big, systemic problems in our software systems.

You’ll learn how to leverage scientific thinking, manage the analysis process, and apply literate statistical programming to analyze software systems in an understandable way. Or to put it in the words of software developers: We automate the analysis of large software systems using open-source tools like Jupyter Notebook, Python, pandas, jQAssistant, and Neo4j. I’ll also demonstrate through live-coding how we can gain new insights from data sources like Git repositories, performance measurements, or source code.

Join me in this session to acquire your starter kit for uncovering deeply hidden issues and change the way of improving systems with data-driven software analysis!

Markus Harrer

March 15, 2024
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  1. Software Analytics with Data Science on Software Data BOBKONF 2024,

    15.03.2024 Markus Harrer Software Evolutionist @ INNOQ Social: markusharrer.de
  2. THE

  3. Frequency Questions Importance Answering YOUR SPECIFIC questions Use standard tools

    for general questions Option 2: Use Software Analytics to answer your very specific questions! Option 1: Just ignore the other questions
  4. JUDGMENT DAY Typical ISSUES TO TERMINATE  Spotting parts in

    source code no one knows of anymore  Finding root causes of performance bottlenecks  Identifying alternative modularization options  Showing the progress of long-living restructurings  Measuring the community activity around open source software  <your very specific analysis in your very specific situation>
  5. = A WAY TO IMPLEMENT SOLID SOFTWARE ANALYTICS R E

    P R O D U C I B L E D A T A S C I E N C E open comprehensible systematic automated
  6. 3 Python 1 ... and matplotlib, numpy, scikit-learn, NLTK, Pygments,

    py2neo, requests, BeautifulSoup, Pygal ...
  7. :Class :Method :Field https://github.com/JavaOnAutobahn/spring-petclinic public class Pet { private LocalDate

    birthDate; public LocalDate getBirthDate(){ return this.birthDate; } public void setBirthDate(LocalDate birthDate){ this.birthDate = birthDate; }
  8. :Class :Method :Field :Entity @Entity @Table(name = "pets") public class

    Pet { https://github.com/JavaOnAutobahn/spring-petclinic
  9. 16 types 17 findings 15 changes 70% usage 5 types

    39 findings 51 changes 80% usage A perspective, where also managers can reason about!
  10. The return of reason The Two Tips The fellowship of

    the bling A u t o m a t i o n Meta Metric Number of solved problems NO Tool To Rule Them All O p e n e s s become the of the T by analyzing software in a data-driven way
  11. More on Software Analytics Adam Tornhill: Software X-Ray Tim Menzies,

    Laurie Williams, Thomas Zimmermann: Perspectives on Data Science for Software Engineering Christian Bird, Tim Menzies, Thomas Zimmermann: The Art and Science of Analyzing Software Data
  12. More on Data Science Jeff Leek: The Elements of Data

    Analytic Style Roger D. Peng: Report Writing for Data Science in R Wes McKinney: Python for Data Analysis
  13. More on Graph Analytics Mark Needham & Amy Hodler: Graph

    Algorithms https://neo4j.com/product/graph-data-science/
  14. Thank you very much! innoQ Deutschland GmbH Krischerstr. 100 40789

    Monheim am Rhein Germany +49 2173 3366-0 Ohlauer Str. 43 10999 Berlin Germany Ludwigstr. 180E 63067 Offenbach Germany Kreuzstr. 16 80331 Munich Germany Gewerbestr. 11 CH-6330 Cham Switzerland +41 41 743 01 11 Albulastr. 55 8048 Zurich Switzerland innoQ Schweiz GmbH Markus Harrer [email protected] @feststelltaste