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Enterprise AI Measurement Guide

Quality

Team Level Breakdown

How do we see each engineer's code quality fields at the individual level per week, including review behavior as both author and reviewer?

AI Code Share and quality metrics dashboard showcasing individual engineer performance with graphs and quality tiers.

What it shows

The Quality Team Level Breakdown is the most granular engineering quality table in Larridin, showing seventeen quality dimensions per engineer per week, including AI Code Share, code turnover at 30 and 90 days, PR size, review pushback rates, comment quality signals, rubber stamp rate both as author and reviewer, the AI Slop Index score, and unit test rate. Every dimension that the sixteen Quality metric tabs surface at the org level is available here at the individual contributor level, week by week.

Why it matters

This table is the engineering accountability layer that makes individual quality conversations possible without relying on manager judgment alone. An engineer with a 0% rubber stamp rate as an author but a 78% rubber stamp rate as a reviewer is behaving differently in the two roles, a signal invisible in aggregate quality metrics. An engineer whose AI Code Share jumped from 20% to 65% in one week while their 30D Turnover also jumped is a training conversation, not a performance conversation. Per-engineer, per-week data at this resolution makes those distinctions actionable.

The Larridin angle

Larridin's per-engineer quality scorecard distinguishes Author and Reviewer behaviors separately for pushback, comment quality, and rubber stamp rate, recognizing that how an engineer writes code and how they review it are different dimensions of quality, and that AI affects them differently. Most engineering platforms measure one or neither.

Related Quality Metrics

Common questions

What is the AI Code Share percentage in the Quality Team Level Breakdown?

The AI Code Share percentage indicates the proportion of code contributions made with AI assistance for each engineer, tracked weekly.

How does the Quality Team Level Breakdown help in understanding code review behaviors?

It distinguishes between author and reviewer behaviors, providing insights into pushback rates, comment quality, and rubber stamp rates separately.

Why is the AI Slop Index score important?

The AI Slop Index score helps assess the quality and effectiveness of AI-assisted code, offering a measure of how well AI is integrated into the coding process.

How can this scorecard impact engineering management decisions?

It provides detailed, per-engineer data that supports informed quality discussions, enabling management to address training needs and performance issues effectively.

See how your organization measures up

Larridin turns every metric in this guide into a live, benchmarked dashboard for your org. No spreadsheets, no manual surveys.