Review load distribution

What the measure captures

Review load distribution shows how review requests or completed reviews are spread across people, teams, repositories, or change types. It is most useful when the team states the unit of analysis, the start event, the completion event, and the population being measured. A review request, a pull request, and a merge are related events, but they are not interchangeable. Keeping those boundaries visible prevents a dashboard from turning an operational signal into an unexplained score.

A practical example

A distribution can reveal that two maintainers handle most approvals while several listed reviewers receive little work, creating continuity risk. Start with a small sample and inspect the underlying requests before creating a target. Record the repository, change type, reviewer path, and relevant policy state. That makes it possible to explain an unusual result instead of simply celebrating or escalating a number. Compare similar work over time and annotate major changes in ownership, branch rules, or automation.

How to use it carefully

Even distribution is not always desirable because expertise differs. Use the signal to examine resilience and development opportunities, not to force equal counts. Review metrics work best as prompts for team-level investigation. Combine them with review samples, author and reviewer feedback, escaped defects, rework, and delivery outcomes. Avoid ranking individuals from a single measure because review difficulty, system familiarity, and assignment patterns vary. A healthy process makes useful feedback available at an appropriate time while preserving accountability for the final change.

How this relates to Weave

Weave can connect review events with change context and delivery signals so teams can inspect review load distribution alongside review time, pull request size, and flow. That context helps teams investigate queues and quality patterns without treating one review number as a verdict on an engineer.