Software Analytics with Jupyter, Pandas, jQAssistant, and Neo4j (Neo4j Online Meetup)

Software Analytics with Jupyter, Pandas, jQAssistant, and Neo4j (Neo4j Online Meetup)

Let’s tackle problems in software development in an automated, data-driven and reproducible way!

As developers, we often feel that there might be something wrong with the way we develop software. Unfortunately, a gut feeling alone isn’t sufficient for the complex, interconnected problems in software systems.

We need solid, understandable arguments to gain budgets for improvement projects or to defend us against political decisions. Though, we can help ourselves: Every step in the development or use of software leaves valuable, digital traces. With clever analysis, these data can show us root causes of problems in our software and deliver new insights – understandable for everybody.

If concrete problems and their impact are known, developers and managers can create solutions and take sustainable actions aligned to existing business goals.

In this meetup, I talk about the analysis of software data by using a digital notebook approach. This allows you to express your gut feelings explicitly with the help of hypotheses, explorations and visualizations step by step.

I show the collaboration of open source analysis tools (Jupyter, Pandas, jQAssistant and, of course, Neo4j) to inspect problems in Java applications and their environment. We have a look at performance hotspots, knowledge loss and worthless code parts – completely automated from raw data up to visualizations for management.

Participants learn how they can translate their unsafe gut feelings into solid evidence for obtaining budgets for dedicated improvement projects with the help of data analysis.


Markus Harrer

November 23, 2017