How to Distribute Code Review Load Fairly Across Your Engineering Team
PublishedWorkflow2,272 words11 min readMay 26, 2026
Two engineers doing 70 percent of the reviews is not a people problem. It is a visibility and routing problem. Here is how engineering teams actually fix code review load distribution without hurting quality.
How to Distribute Code Review Load Fairly Across Your Engineering Team
Ask any engineering manager how their code review workload is distributed and most will say something like "pretty evenly, I think." Then you pull the actual data and find out two people reviewed 70 percent of the PRs last quarter. The rest of the team barely showed up.
This is one of the most consistent patterns across engineering teams of every size. Review work concentrates on the same few people, usually the most senior engineers, and nobody notices until one of them burns out, goes on vacation, or leaves the company.
Distributing code review load fairly is not complicated. But it requires making the invisible visible and being intentional about a process most teams have left on autopilot.
Here is what actually works.
Why Code Review Load Gets Concentrated in the First Place
The pattern is predictable. When a PR needs a reviewer, the path of least resistance is to tag whoever you trust most. On most teams, that is the same two or three senior engineers who know the codebase well, give good feedback quickly, and approve things without drama.
The junior and mid-level engineers get tagged occasionally, but not consistently. Over time the gap widens. The senior engineers who review everything build deeper context. The ones who rarely review build less. The imbalance becomes self-reinforcing — you keep tagging the people with the most context because they are the most useful reviewers, and they build more context because they are doing all the reviews.
The cost of this is distributed across the whole team in ways that are hard to see in any single day. Your two best reviewers spend a significant portion of their time doing reviews instead of building. Your junior engineers do not develop the reviewing instincts that would make them better at their own code. And the whole pipeline runs on a dependency that breaks the moment those two people are unavailable.
Academic research from 2023 examining code review concentration found that review workload is highly concentrated on most software projects even when that concentration is unintentional. It happens by default, not by design. Changing it requires a different default.
The Problem With Round-Robin Assignment
The obvious fix is round-robin — just rotate who gets assigned. GitHub supports this through team-based review assignment. You define a team, set it to round-robin distribution, and GitHub automatically assigns the next person in the rotation to each new PR.
Round-robin solves the distribution problem on paper. In practice it creates a different problem. It assigns reviews without regard for who actually knows the code being reviewed. A backend engineer gets assigned a frontend PR they have no context for. A junior engineer gets assigned an architectural change that is beyond their current depth. The review that comes back is either shallow or delayed while the reviewer tries to build context they do not have.
Fair distribution is not the same as equal distribution. Equal means everyone gets the same number. Fair means everyone gets reviews they can actually do well, and no one person carries an unsustainable share of the total load.
The goal is to route reviews based on code familiarity and current workload simultaneously — not just one of those factors.
What Fair Code Review Distribution Actually Looks Like
A team with healthy review distribution has a few recognizable characteristics.
No single engineer is doing more than 25 to 30 percent of the team's total reviews over a two week period. If someone is at 50 percent or above, the load is already concentrated enough to be a problem.
Junior and mid-level engineers are reviewing regularly, not just occasionally. Their reviews might be more limited in scope — catching obvious issues, validating test coverage, checking that the PR description matches the implementation — but they are participating and building the skill.
Senior engineers are still reviewing, but they are focused on the things only they can evaluate. Architectural decisions, cross-system impact, changes to core infrastructure. Not every PR needs a senior review. Most PRs need a good review from someone with context, and context is not exclusively owned by the most senior people.
Review assignments are made based on who knows the relevant part of the codebase, not just who is available or who gets tagged most often. On a team with six engineers, multiple people usually have relevant context for any given PR. The assignment logic should prefer the person with the most recent and relevant file history who also has the lightest current review load.
How to Actually Fix the Distribution Problem
The starting point is measurement. You cannot fix a distribution problem you cannot see. Most teams have no visibility into how review work is being distributed. GitHub does not surface this by default. You need to look at who is being assigned reviews, how often, and what the outcome is.
Once you can see the data, most teams find the problem is worse than they thought. When a team I spoke with pulled their review data for the first time, they found one engineer had reviewed 68 percent of all PRs in the previous month. She was not complaining. She was just the person everyone trusted. The rest of the team had no idea.
Making review load visible is often enough to start changing the behavior. When your team can see the distribution in a weekly leaderboard or a simple report, people naturally start to self-correct. Engineers who have been reviewing heavily start declining or deferring. Engineers who have been reviewing rarely start picking things up. The visibility creates accountability without anyone having to have an awkward conversation.
After visibility, the next lever is routing logic. If your review assignments are being made manually by the PR author, you are going to get the same concentrated pattern every time because authors default to the people they trust. Automated routing that considers both code familiarity and current workload will distribute reviews more evenly without sacrificing quality.
The third lever is team norms. A team that has explicitly agreed that reviews should be distributed, that everyone should be reviewing regularly, and that no one person should carry more than a third of the load will behave differently than a team that has never talked about it. Norms have to be stated to be followed.
Building Knowledge Through Better Distribution
There is a dimension to fair code review distribution that goes beyond workload. When review work is concentrated on a few engineers, knowledge is also concentrated. The engineers who do all the reviewing develop a deep understanding of how the entire codebase works. The ones who rarely review understand their own work and not much else.
This is a fragile setup for any team. If one of your concentrated reviewers leaves, you lose both their review capacity and the knowledge they built through years of reviewing. That knowledge is not documented anywhere. It exists in their heads because they have seen every significant change to the codebase for the past two years.
When reviews are distributed, knowledge spreads. A junior engineer who reviews twenty PRs in areas outside their primary ownership will develop context they would never have built otherwise. They become more capable reviewers and better engineers. The team's bus factor goes up. The dependence on any single person goes down.
Research on code review recommendation shows that distributing review assignments based on code history and knowledge rather than seniority alone actively reduces knowledge silos and mitigates the risk of developer turnover. The review process, when distributed well, is a continuous knowledge transfer mechanism.
Tiered Review: A Practical Model for Growing Teams
One approach that works well as teams grow is tiered review. Not every PR needs the same level of scrutiny, and not every engineer is equipped to provide every type of review.
The first tier covers peer review. Any team member with relevant domain knowledge can do this. They check that the code does what the PR description says it does, that test coverage is reasonable, that the implementation is clear and not unnecessarily complex. This tier catches most bugs and most obvious quality issues. It does not require seniority. It requires context in the relevant part of the codebase.
The second tier covers senior review. This is reserved for PRs that touch core infrastructure, establish new patterns, or have significant cross-system impact. Senior engineers review at this tier not because they are the only ones who can provide feedback, but because they are best positioned to evaluate the long-term implications of the change.
Most PRs only need tier one review. Teams that route everything through tier two create the concentration problem by design. Separating these two types of review and being explicit about which one each PR needs is one of the most effective ways to distribute load without sacrificing quality.
Reviewer Rotation Without the Downsides
Pure rotation does not work because it ignores context. But no rotation at all produces concentration. The middle ground is guided rotation — automated assignment that uses code history and current load as primary signals, with rotation as a tiebreaker.
In practice this means: when multiple engineers have relevant context for a PR, assign to the one with the lighter current review load. When one engineer clearly has more context than the others, assign to them regardless of rotation — but track their load and reduce their assignment rate in subsequent weeks to compensate.
This approach means that reviews go to people who can do them well while preventing any one person from becoming the default for everything. It requires tooling to implement properly because doing this calculation manually for every PR is not sustainable.
The teams that have implemented load-aware routing consistently report that their review distribution evens out over four to six weeks, with no significant increase in review cycle time. The reviews are just as fast. They are done by a wider range of people.
What Happens to Review Quality When You Distribute Load
The concern most engineering managers have when they think about distributing reviews more broadly is that quality will drop. If the senior engineers are not reviewing everything, more bugs will get through.
The data does not support this concern. Engineering teams that ignore proven practices around review load distribution lose 20 to 40 percent of their velocity to slow, unfocused reviews according to multiple engineering productivity studies. Concentration creates a bottleneck, and bottlenecks reduce both quality and speed.
When load is distributed well, review quality tends to go up, not down, because reviewers are less overloaded. An engineer reviewing three PRs a day provides better feedback on each one than an engineer reviewing ten. The reviews that the concentrated reviewers were doing when they were at capacity were often shallow approvals rather than genuine reviews. Distributing the load makes every review better.
The one genuine risk is that less experienced reviewers miss things that a senior would catch. This is real but manageable. You mitigate it through tiered review — ensuring that PRs with significant architectural implications get senior eyes — and through building reviewer capability over time by having junior engineers do more reviewing.
The Leaderboard Effect
Something interesting happens when teams make review distribution visible through a leaderboard. The change in behavior is almost immediate and almost never feels coercive.
Engineers who see that they are low on the leaderboard start picking up reviews not because anyone told them to but because the social visibility creates a mild motivation to contribute. Engineers who see they are at the top start deferring reviews to teammates more naturally. The leaderboard does not shame anyone. It just makes the distribution visible in a way that is easy to act on.
Teams using leaderboards for code review participation consistently report that their review rate — the percentage of PRs that receive a timely review — climbs significantly within the first few weeks. Not because anyone changed the process. Just because visibility changed behavior.
The same principle applies to any metric that surfaces review load. When people can see the distribution, they correct it. When they cannot see it, the default pattern persists.
Starting With One Change
If your team has a code review distribution problem, the fastest single change you can make is to make the distribution visible.
Pull the review data for the last four weeks. Show the team who has been reviewing and how much. That one conversation often changes the pattern within the next sprint without any process changes at all. People were not distributing reviews unfairly on purpose. They just had no way to see that the load was uneven.
From there, the next step is to set a norm — something like "no single person should handle more than 30 percent of team reviews in any two-week period" — and make that norm visible in your weekly reports.
After that, if the distribution still does not even out naturally, look at your assignment process. If reviews are being manually assigned by PR authors, switching to automated load-aware routing will make the biggest difference with the least amount of ongoing effort.
Fair code review distribution is not a complicated problem. It is a visibility problem. The moment your team can see the distribution, they will start fixing it.
PRBoard routes pull requests to the right reviewer based on workload and code familiarity, and makes review load visible with a weekly leaderboard. Free for teams of up to three people. prboard.io
