AI in engineering

Why DORA wasn’t enough, and what came next

Part II: The evolution of developer measurement

Nov 4 • Justin Reock and Scott Carey

You can’t find 10x developers, but you can build 10x organizations

Part I: The Coding War Games, a study behind the 10x developer myth.

Oct 28 • Justin Reock and Scott Carey

AI made developers faster. Is their judgment keeping up?

A study of 331 Microsoft developers on where AI makes them faster, go further, or start afresh.

Oct 21 • Brian Houck and Scott Carey

The gap between code velocity and trust is growing

Throughput is up 75.5% in a year, but organizations with the most AI-authored code wait 105% longer for a first review.

Oct 14 • Grace Fu and Scott Carey

Don’t measure AI Code Percentage on its own

Treat it like a lens, not a KPI.

Oct 7 • Scott Carey and Brian Houck

Do you really need another framework?

Where CAFE(S) fits with your existing measurement and evaluation stack.

Sep 30 • Brian Houck

Introducing CAFE(S): A framework for defining AI context quality

Five durable properties for evaluating the context we hand to AI agents.

Sep 24 • Brian Houck

Your agent doesn't have a model problem

The context problems humans have been quietly fixing for decades and what happens when agents inherit them.

Sep 16 • Brian Houck

AI accelerates output, not innovation

AI output explains 63% of developer time savings but shows a weak link to increased innovation ratio.

Sep 9 • Grace Fu

The quality paradox of AI-generated code

What a five-part definition of quality from 2020 tells us about software development in the age of AI.

Sep 2 • Brian Houck

Can “Predictable Delivery” be measured?

Five statistical techniques for turning delivery history into forecasting confidence.

Aug 21 • Brian Houck

Is there a relationship between cycle time and PR throughput?

There’s a closer relationship between the two in high-throughput organizations. In low-throughput organizations, there are other variables at play.

Aug 19 • Brian Houck and Grace Fu

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