Measure AI-Generated Code

Measure AI-generated code you can actually defend

Tag every change by provenance and track how AI-written code holds up against human code across quality, rework, and failure rate.

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Tag code at the source

Attribute every diff to a human author, an AI agent, or a hybrid session so provenance is recorded before it ever reaches main.

AI vs human quality deltas

Compare defect density, revert rate, and review churn side by side so you can see where AI-generated code actually holds up.

PR-level provenance

Break down each pull request by how much of it was model-authored versus edited by an engineer, right down to the line.

Rework and revert tracking

Watch how long AI-written code survives in production and how often it gets rewritten within the following weeks.

Quality gates, not vibes

Set thresholds for test coverage, review depth, and change failure rate that apply equally to agent output and human output.

A metric you can defend

Report AI code impact with methodology attached, so the number survives scrutiny from engineers and finance alike.

The metric nobody reports credibly

Every engineering org is now shipping AI-generated code, and almost none of them can say whether it is better, worse, or riskier than what their humans write. The numbers that get quoted - suggestion acceptance rate, lines generated, tool seat adoption - describe usage, not quality. They inflate easily and collapse under any real audit.

Measuring AI-generated code credibly means tagging provenance at the source and then following that code through its entire life: how much survives, how often it is reverted, how much review it needed, and whether it caused failures downstream. That is the difference between a slide and a signal. Our measurement product is built to produce the second kind.

Once provenance is recorded, AI code stops being a black box. You can finally answer the question your CTO is actually asking: is this making us faster without quietly making us more fragile? Compare the two populations directly with PR quality analytics.

From provenance to outcomes

FeatureFactory attributes each change to a human, an agent, or a hybrid session, then layers outcome data on top: change failure rate, revert and rework rate, defect density, and cycle time. Because we use the same yardstick for AI and human code, the comparison is honest instead of flattering.

This connects directly to the metrics you already trust. AI-generated code that lands cleanly and stays put improves your DORA metrics; code that ships fast and gets rewritten shows up as elevated rework and degraded cycle time. Nothing hides.

The result is a defensible number with methodology attached - one you can put in front of engineers, engineering leaders, and finance without it falling apart. Start with a free AI vs human PR analyzer, then wire up continuous measurement across your factory.

Related tools & solutions

Measure AI vs human code quality AI coding metrics AI vs human PR analyzer PR quality analytics AI code review

Frequently asked questions

FeatureFactory tags code by provenance at commit and PR time, then tracks quality signals over its lifetime: defect density, revert rate, review churn, and change failure rate. Instead of a single vanity percentage, you get a lifecycle view of how model-authored code behaves versus human-authored code. See how measurement works.

From our design partners

“We finally have one number for whether the AI-written PRs are actually good. It changed how we staff reviews.”

SStaff EngineerSeries B fintech

“Plan from real signal, ship with agents, then see if the metric moved. That loop is the whole point.”

EEng ManagerDeveloper tools

“The measurement is transparent and the code is ours. That was the dealbreaker with the enterprise options.”

VVP EngineeringHealthcare SaaS

Measure what you ship.

Connect one repository and get your first delivery baseline.

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