Explore prioritizing technical debt in software development through a data-driven approach in this conference talk. Learn how to leverage version control data to uncover organizational patterns and make informed decisions about code improvements. Discover techniques for identifying legacy code, analyzing microservice architectures, and balancing short-term and long-term goals. Gain insights from real-world case studies of Android, Linux Kernel, and .NET Core Runtime. Understand the impact of team structures on code quality and learn how to use behavioral data to optimize software architecture. Apply these strategies to your own codebase and improve developer productivity while managing technical debt effectively.
Prioritizing Technical Debt as if Time and Money Matters
Overview
Syllabus
Intro
Lehman's "Laws" of Software Evolution
QUANTIFYING TECHNICAL DEBT?
THE PERILS QUANTIFYING TECHNICAL DEBT
Version-Control - A Behavioral Data Source
Case Study: Android
X-Ray of ActivityManager Service.java
Code Quality In Context: Why you shouldn't fix all code issues
What Is Legacy Code?
Case Study: Off-Boarding
Case Study: ASP.NET MVC Core
Tooling: Try it on your own Code
Analysing Microservice Architectures
Aggregation: Architectural Hotspots in Spinnaker
Microservice Dependencies: The Impact of Change
Change Coupling: Component or Feature Teams?
Dependencies and Teams: Locality of Change
Re-Think Software Architectures: From Accidental to Essential
Taught by
GOTO Conferences
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