Prioritize Technical Debt as if Time and Money Matter

Prioritizing technical debt is a hard problem as modern systems might have millions of lines of code and multiple development teams — no one has a holistic overview. In addition, there's always a trade-off between improving existing code versus adding new features so we need to use our time wisely.

What if we could mine the collective intelligence of all contributing programmers and start making decisions based on information from how the organization actually works with the code?

In this workshop, you'll learn how easily obtained version-control data lets you uncover the behavior and patterns of the development organization. This language-neutral approach lets you prioritize the parts of your system that benefit the most from improvements so that you can balance short- and long-term goals guided by data.

In this session, you’ll learn:

To prioritize technical debt in large-scale systems
Balance the trade-off between improving existing code versus adding new features
Visualize long time trends in technical debt
Take a data-driven approach to technical debt.

During this workshop, you get access to CodeScene – a behavioral code analysis tool that automates the analyses – which we use for the practical exercises. We’ll do the exercises on real world codebases in Java, C#, JavaScript and more to discover real issues.

Participants are also encouraged to take this opportunity to analyze their own codebase to get actionable take-away information.