Explore techniques for prioritizing technical debt in large-scale software systems through this insightful conference talk. Learn how to leverage version control data to uncover development patterns and make informed decisions about code improvements. Discover a language-neutral approach to balancing short-term and long-term goals in software development, illustrated with real-world examples from Android, Linux Kernel, and .NET Core Runtime. Gain a new perspective on software evolution, legacy code, and the importance of context in addressing code quality issues. Understand how to quantify technical debt, identify hotspots in your codebase, and mitigate risks associated with developer off-boarding. Examine case studies from various projects and learn practical tools to apply these concepts to your own code.
Prioritizing Technical Debt Using Version Control Data
Overview
Syllabus
Intro
Lehman's "Laws" of Software Evolution
Are We Treating Symptoms Instead of the Real Issues
QUANTIFYING TECHNICAL DEBT?
THE PERILS QUANTIFYING TECHNICAL DEBT
Version-Control - A Behavioral Data Source
Case Study: Android
Actionable Insights?
Hotspots: X-Ray ActivityManager Service. Java
X-Ray of ActivityManager Service.java
Why You Don't Have To Fix All Technical Debt
Code Quality In Context: Why you shouldn't fix all code is!
What Is Legacy Code?
The Technical Debt That Wasn't
Software Evolution power laws are everywhere
Case Study: Off-Boarding
Case Study: ASP.NET MVC Core
Mitigate off-boarding risks
There's More to Code Complexity than Code
Tooling: Try it on your own Code
Taught by
ChariotSolutions
