Hello, everyone. My name is Rebecca Parsons. I'm the Chief Technology Officer Emerita for ThoughtWorks, and I'm joined today with three guests to talk about the topic of AI refactoring.

And first, I'd like to introduce Adam, who is the founder of Codecene.

Adam, please say hello to everybody.

Hello, everybody. Really good to be here.

And Berghita, a principal technologist with ThoughtWorks, who is leading our efforts

in AI-assisted software delivery. Bergeen. Hi, everybody. And Martin Fowler, our chief scientist,

author of the book Refactoring, and we are here to discuss AI refactoring in all its glory. One of the things that

had disturbed me very early in many of the studies that were done on the power of tools like

GitHub co-pilot was they focused on speed and they focused on task

completion.

And no one ever said anything about code quality and nobody ever said anything about, and

oh, by the way, how is this going to help us with all of the legacy code that we have? And so that's the topic that we are going to discuss today.

And so let's start with the question.

How do we characterize code quality?

What does it mean for code to be good code or bad code?

The interesting thing with code quality is that there's not a single metric that can

capture a multifaceted concept like that.

I mean, no matter what metric could come up with, there's always a way around it or

there's always a counter case.

So what we've been working on for like the past six to seven years is to develop the code

half metric.

And the idea with code half is that instead of looking at a single metric, you look at

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