Hey 👋 this is Luca! Welcome to a 🔒 weekly edition 🔒 of Refactoring.
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Last week we explored software delivery metrics and how they predict overall engineering efficiency.
This week I follow up, with a broader scope, to discuss my favorite metric of all: Cycle Time.
📈 The Rise of Engineering Analytics
Over the last couple of years, several analytics tools rose to support a data-driven approach to Software Engineering.
You may (or may not) know a few of them: LinearB, Code Climate, Waydev, Flow.
They connect to your code hosting (e.g. Github) and automatically create measures based on commits, PRs, and releases.
Among these measures, I would argue that Cycle Time is the most significant one. To the point that if you had to focus on just one KPI for your dev process, it should probably be it.
Let's see what it's all about.