Is your AI-authored code actually good?

Every AI coding tool markets acceleration: more pull requests, more lines, faster merges. But raw speed is a trap if the code comes back as rework, reverts and midnight hotfixes. AI coding metrics exist to measure the trade-off, putting quality, rework and speed side by side so a fast quarter is not quietly a fragile one.

FeatureFactory scores AI-authored changes on the signals that predict durability: test coverage deltas, review depth, change size, and how much of that code survives untouched a week later. When AI output churns, you see it. When it holds, you can prove the acceleration was real. Dig into the mechanics in our AI vs. human code quality guide.

The goal is not to distrust AI. It is to make its impact measurable and defensible so engineering leaders can invest with evidence instead of vibes. See how it fits your workflow on the measure product page.

Measuring AI coding starts with honest attribution: which changes were AI-assisted, and how do they compare to your team's hand-written baseline? FeatureFactory reads git, your pull-request platform and CI to build that comparison automatically, then tracks cycle time, rework rate and quality by author type. No spreadsheets, no self-reporting, no gaming.

From there, the same data powers broader engineering intelligence. Pair AI coding metrics with DORA metrics and cycle time analytics to see whether AI-driven speed actually reaches production without inflating failure rates. Fold PR quality analytics in and every merge carries a quality read, not just a green check.

The result is accountable acceleration: your team ships faster with AI and you can show, quarter over quarter, that the code held up. Compare approaches on our comparison hub or start with a product tour.