PR Quality Analytics

PR quality analytics for every pull request, every repo

Per-PR quality scores, effort multipliers and review-quality tiers - auditable signals that measure the work, not the people.

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A quality score for every PR

Each pull request gets a per-PR quality score from signals like test coverage delta, review depth, revert risk and change surface - not a gut-feel thumbs up.

Effort multipliers

See how much real effort a change took relative to its size, so a deceptively small but risky PR is not mistaken for a trivial one.

Review-quality tiers

Classify reviews into tiers - rubber-stamp, skim, or deep - based on comment substance, turnaround and lines actually inspected per approval.

Every repo, one view

Roll PR quality up across every repository and team, then drill from the org scoreboard down to a single diff without switching tools.

AI vs human PR quality

Attribute quality scores to AI-generated and human-authored pull requests separately, so you know whether coding agents raise or lower your bar.

Catch risk before merge

Flag high-risk PRs - large blast radius, thin review, missing tests - early enough to add scrutiny instead of finding out in the incident channel.

From merged-and-forgotten to measured

Most teams treat a merged pull request as a finished thing - approved, shipped, invisible. But merge is exactly where the useful signal lives. FeatureFactory attaches a per-PR quality score to every pull request, so a rushed rubber-stamp and a carefully reviewed change stop looking identical in your history.

The score is built from signals engineers already respect: did tests come with the change, how deep was the review, how large was the blast radius, and did the change get reverted or hotfixed soon after. Because each score is auditable, nobody has to trust a black box. Explore how it rolls into full pull request analytics across your org.

The payoff is a scoreboard for the work itself, not the people. You can see which repos ship clean and which quietly accumulate risk, then take the product tour to see how the signals connect.

Effort multipliers and review tiers, across every repo

Raw diff size lies. A 400-line generated file is trivial; a 15-line change to an auth boundary is not. Effort multipliers weight each PR by the real work and risk it carries, so small-but-dangerous changes get the scrutiny they deserve and volume stops masquerading as productivity.

On top of that, review-quality tiers tell you whether approvals are substantive or reflexive - separating deep reviews from skims and rubber stamps without ever ranking a reviewer by name. Together they answer the question every engineering leader actually has: is our review process protecting us, or just adding latency? Pair it with the Measure product and engineering metrics for context.

All of it aggregates across every repository and team into one view, then drills down to a single diff. If you are weighing tools, see how we compare to LinearB on depth of PR-level signal.

Related tools & solutions

Pull request analytics AI code review Code quality dashboard The Measure product Code review best practices AI vs human PR analyzer

Frequently asked questions

Each score blends objective signals pulled from your version control and CI: test coverage delta, review depth, comment substance, change surface, revert and hotfix history, and turnaround. Every score is transparent about its inputs, so you can trace it back to the specific pull request that produced it. See how it connects to broader pull request analytics.

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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