How many people should review a pull request on a small team?

A small development team should choose the number of pull request reviewers based on the risk of the change, not a fixed rule. Low-risk changes may need no human reviewer once automated checks pass, ordinary product changes usually need one, and high-risk changes involving authentication, payments, database migrations or infrastructure may need two. Anthropic's Claude Code team published this risk-based approach on 15 October 2026.

Reviewer count is only half the problem. Teams also need a clear response-time target, especially when developers work across time zones. A review that waits overnight for its first response, then waits another day for each round of changes, can delay a feature far longer than the coding itself.

Last updated: 2026-10-18

Key facts

In this article

• Why does the usual code review process break on a small remote team?
• What does a code review actually catch, and where does the time go?
• How many reviewers does each kind of change need?
• What turnaround should you promise each other?
• What does the first month of a code review process for a small team look like?
• Where RocketDevs fits
• Conclusion: make code review a system, not a bottleneck
• Frequently asked questions

Why does the usual code review process break on a small remote team?

Because every change goes through the same process, regardless of its risk, and every round of comments can mean waiting until the other person wakes up. In a study by Herbsleb and Mockus, distributed work items took about two and a half times as long as comparable work handled by co-located teams. For a small remote team, code review can become one of the biggest sources of delay.

Teams of three to six developers often inherit their review process rather than design it. Someone sets the rule to require one approval, usually in the first week, and the most senior developer becomes the default reviewer. Nobody revisits the arrangement as the team grows or changes. It works when the team handles ten pull requests a week within one time zone. It starts to break down when developers are eight hours apart and AI coding tools increase the number of pull requests each person produces.

The hero reviewer

The first problem is not a tool. It is a person. On many small teams, one developer, often the technical founder or first engineer, knows the codebase better than anyone else. As a result, nearly every pull request ends up in their queue. They review carefully, but that care creates a bottleneck: the entire team's progress depends on one person's available attention. When that person takes a week off, reviews can grind to a halt. It is the same problem as having one person who knows everything, except the bottleneck appears at the merge button.

Research shows how costly reviewer problems can become. Thongtanunam and colleagues examined 42,045 reviews across four open-source projects and found that reviews affected by reviewer-assignment problems took 12 days longer to approve. Small teams may not have a shortage of qualified reviewers across the organisation. Instead, they often have one person everyone relies on, with more work waiting than that person can review.

The pressure is increasing as developers produce more code. GitHub's Octoverse 2025 report says developers merged an average of 43.2 million pull requests per month in 2025, up 23% year on year. Inside companies, the increase can be even more pronounced. Meta researchers report that the volume of code changes per developer rose 51%, with agentic AI accounting for most of the growth, while the share of changes receiving timely review declined. Anthropic has also reported that changes landing each day in its monorepo rose from about 2,500 in May to more than 30,000 in September.

Data from engineering analytics provider Faros AI points to the same tension. Its analysis of telemetry from more than 10,000 developers found that teams with high AI adoption merged 98% more pull requests, while time spent reviewing code rose 91%. More code is moving through the pipeline, but reviewing that code still requires attention.

The wider research suggests that producing code faster does not automatically mean delivering software faster or more reliably. DORA's 2024 research found that a 25% increase in AI adoption was associated with a 3.1% increase in code review speed, alongside an estimated 1.5% decrease in delivery throughput and a 7.2% decrease in delivery stability.

In Stack Overflow's 2025 Developer Survey, 46% of respondents said they distrusted the accuracy of AI tools, compared with 33% who trusted it. In the same survey, 45% said debugging AI-generated code took more time. That extra checking work lands on the people reviewing the changes. The result is a growing gap between how much code developers can produce and how much their teammates can reliably assess. For more on the financial consequences, see the hidden cost of AI-generated code.

There is an uncomfortable implication for small teams, particularly companies planning to hire their way out of a delivery slowdown. If one senior developer is already struggling to review everyone's work, adding more developers can make the bottleneck worse. Each new person may produce more pull requests, but those changes still enter the same queue. Fix the review process first, then consider whether adding people will actually improve delivery.

Every round trip costs a day

The second problem is the time-zone arithmetic. Imagine a developer in New York working with a reviewer in Lisbon, five hours apart, or a developer in Lagos working with a reviewer in San Francisco, eight hours apart. The author opens a pull request at 4 p.m. The reviewer has already finished work for the day, so the request waits until the next morning. The reviewer reads the code and asks for changes. The author sees those comments the following morning, makes the fixes by lunchtime and pushes an update. By then, the reviewer may be offline again. The next review begins the following day.

With an eight-hour difference and little or no working-hour overlap, two rounds of changes can stretch a pull request across two or three calendar days, even if the code itself is relatively small. The main cost is not necessarily the time spent writing or reviewing code. It is the time each person spends waiting for the other to respond.

Compare that with a team working in the same location. In a study covering nine million reviewed changes, Sadowski and colleagues at Google reported that the median time for the full review process was under four hours, with initial feedback arriving in under an hour for small changes. They also found that a change usually needed only one reviewer. The review process itself was not fundamentally different. The time available for people to respond was.

That does not mean distributed teams produce worse code. Microsoft researchers comparing Windows Vista components developed by distributed and co-located teams found a negligible difference in failures. A follow-up study of Office 2010 also found differences too small to concern practitioners. The evidence points to a distinction worth keeping clear: working across locations can increase the time it takes to complete work without necessarily reducing software quality.

The practical fix is therefore to improve how reviews are assigned, prioritised and handed between people, rather than assuming the answer is to hire more developers or abandon remote work. That makes code review part of the same operational problem as handover delays across time zones: the team needs a process that keeps work moving when its members are not online at the same time.

What does a code review actually catch, and where does the time go?

Code review is better at improving maintainability and sharing knowledge than it is at finding functional bugs. In a Microsoft study, comments about defects made up only about one-eighth of all review comments. Another study found that 75% of defects discovered during review did not affect the software's visible behaviour. Meanwhile, much of the time spent in a review cycle is not spent reading code. It is spent waiting for a person to respond.

To improve code review, a small team needs to understand which parts of the process create value and which parts create delays. They are not necessarily the same.

Wait time, not reading time

A pull request moves through seven stages: it opens, automated checks run, a reviewer picks it up, comments arrive, the author makes changes, the reviewer checks those changes, and the pull request is merged. Actual reading happens at only a couple of these stages. At the others, the pull request is waiting for a machine or a person.

Machine delays are usually short and predictable. Continuous integration (CI), which automatically builds and tests the code, generally runs in minutes. Human delays are less predictable, particularly when developers work across time zones.

Kudrjavets and colleagues studied half a million reviews in Gerrit and Phabricator and identified two major sources of delay: the wait between proposing a change and receiving the first response, and the wait between acceptance and merging. They estimated that reducing the second delay alone could shorten Phabricator review times by 29% to 63%, and suggested replacing manual merges with automatic ones.

That second delay is easy for a small team to overlook. Once a pull request has been approved and all required checks are green, leaving it open until someone manually merges it adds waiting without adding another review. Configuring auto-merge to proceed when the required conditions are satisfied removes that unnecessary step.

Another common recommendation is to keep pull requests small. That is good advice because smaller changes are easier to understand and assess. Google's engineering guidance says that 100 lines is usually a reasonable size for a change, while 1,000 lines is usually too large.

SmartBear, a code review tooling vendor, reports that a study involving a Cisco team suggested limiting review sessions to 200 to 400 lines over 60 to 90 minutes, because defect detection declines beyond that point. However, the page does not publish the study's methodology, so treat those specific figures as vendor-reported guidance rather than a universal threshold.

There is an important limitation: smaller pull requests do not automatically merge faster. Kudrjavets, Nagappan and Rastogi analysed 845,316 pull requests from 100 popular GitHub projects and found that pull request size and composition did not correlate with time to merge. Their analysis of another 401,790 Gerrit and Phabricator reviews supported the same conclusion.

The distinction is straightforward. Smaller changes make it easier to review the code properly, but they do not make a reviewer in another time zone available any sooner. Teams need to manage both reviewability and response time.

The first response itself also needs careful measurement. Hasan and colleagues examined 111,094 pull requests across ten open-source projects and found that bots frequently generated the first response. That response may be useful, but an automated comment is not the same as a human reviewing the change. When measuring review delays, track the time to the first human response, not simply the first comment.

What review is good at, and what it is not

Developers often say they review code to find bugs. Research into the comments reviewers actually make shows that defect detection is only part of the picture. This difference between what people expect from review and what it delivers is one of the field's recurring findings.

At Microsoft, Alberto Bacchelli and Christian Bird classified 570 review comments and surveyed 165 managers and 873 programmers. Although finding defects was the most commonly stated motivation for code review, their ICSE 2013 paper found that defect-related comments accounted for about one-eighth of the sample and mostly concerned small-scale or superficial issues. The authors highlighted other benefits, including knowledge transfer, greater team awareness and better ways of approaching a problem.

Other studies support the importance of maintainability. Mantyla and Lassenius examined nine industrial and 23 student code reviews and found that 75% of defects identified during review did not affect visible software functionality. These findings can still matter: a problem may not change what users see today, but it can make the code harder to understand, modify or extend later.

Beller and colleagues found a similar 75:25 ratio between maintainability-related and functional problems when studying two open-source projects. Microsoft engineers made a related argument in their paper Code Reviews Do Not Find Bugs, reporting from internal data that review is often the longest part of code integration and often does not find the functional issues that should block a submission.

None of this means code review is unnecessary. It means teams should understand what it is best equipped to do.

Review also spreads knowledge across a team. Rigby and Bird studied projects at Google, Microsoft and AMD and found that peer review increased the number of distinct files developers knew about by 66% to 150%. For a team of four, that wider understanding can reduce dependence on the one developer who knows a particular part of the codebase.

Skipping review also carries risks. McIntosh and colleagues studied the Qt, VTK and ITK projects and estimated that low review coverage and participation contributed to components with up to two and five additional post-release defects, respectively.

There is also a benefit that automated checks cannot fully reproduce: human judgement. John Allspaw argues that when an experienced engineer says, "I don't understand this," that confusion is itself a finding. The change may pass every test and still be too complicated, poorly explained or unnecessary in the first place. A reviewer can question not only how the code works, but whether the change should exist at all.

For a small team, code review therefore has three distinct jobs:

• Find functional defects. Automated tests and CI should catch as many predictable problems as possible on every change, without waiting for a human.
• Protect maintainability. Human reviewers assess whether the code is understandable, consistent with the existing system and practical to maintain.
• Spread knowledge. More than one person needs to understand the codebase, so review should not always go through the same senior developer.

A process that sends every change to one senior reviewer risks doing all three jobs poorly. Automated checks are underused, human review becomes slow, and the rest of the team learns less about the system.

Risk tiers, from the Anthropic system to a team of four

Anthropic's 15 October article, How the Claude Code Team Does Code Review, describes a review system built to handle a high volume of changes without requiring the same human attention for every pull request. Its reported results come from Anthropic itself, a company selling the product described, so they should be treated as the company's account rather than independent proof. The approach attracted considerable attention: the post received 460 points and 465 comments on Hacker News the following day.

The system uses several layers of checks. Before a pull request opens, an agent runs tests, the build and the linter, while a second agent reviews the changes in a fresh context. Once the pull request is open, required CI checks cover linting, types, tests and packaging. An AI code review and a separate security review follow, with unresolved findings blocking the merge. The process then applies risk-based approval, uses a merge queue to rerun checks against the latest main branch, and releases changes behind a feature flag before broader deployment.

The most useful idea for a small team is the approval system. Anthropic divides changes into three risk tiers:

The key principle is that the change determines the tier, not the person submitting it. Anthropic uses a classifier to assess changes, with certain paths always requiring human review. Only authors on a trusted list can use the lighter review paths.

Anthropic reports that between June and September, merged pull requests to Claude Code more than doubled while the share that had to be reverted fell by more than 40%. It also reports that after changes to how its AI reviewer handled repeat findings, the proportion of pull requests passing after one review round increased from 65% to 78%. The mean number of reviews needed to pass fell from 2.08 to 1.32.

The company also acknowledges limitations. Detecting a bad merge still depends on the author and on-call team, and it does not yet have circuit breakers that automatically tighten approval requirements when reverts increase. Those caveats matter: a risk-based process still needs monitoring and human accountability.

Other organisations have adopted similar ideas. GitLab's public engineering handbook allows a maintainer to delegate approval of qualifying merge requests to an independent review agent under recorded risk criteria. Meta's RADAR preprint describes a risk-stratified system that has automatically reviewed more than 535,000 diffs. Meta reports that its auto-reviewed diffs were reverted at one-third the rate of other diffs. However, because the system selected changes considered low risk, that figure does not prove automated review is inherently safer.

The shared lesson is that review effort should match the risk of the change. Machines can help clear routine work, while humans concentrate on changes where judgement and accountability matter most.

For a team of four, implementing this approach does not require a sophisticated classifier. Start with a written list of high-risk areas, such as authentication, payments, database migrations and infrastructure. The team already knows which directories and systems contain the most sensitive code, so it can agree on the list in an afternoon.

Keep required CI checks in place, because low-risk changes should bypass human approval only when the automated checks are meaningful and reliable. Establish a trusted-author rule as well: a new hire should use at least the medium-risk review path during their first month, even for a minor copy change, until the team is confident in their familiarity with the codebase.

An AI reviewer is optional for a team this size. Anthropic recommends starting small, using it on one repository and tuning the REVIEW.md instructions for a few weeks before expanding its use. Its open-source Claude Code GitHub Action is one possible starting point; it had 9,560 GitHub stars when checked.

Whichever tools you choose, keep automated review separate from human approval. A bot can identify potential problems and run checks, but its response should not count as a human approval when your risk rules require one. The aim is not to eliminate human review. It is to reserve people's attention for the changes that need it most.

How many reviewers does each kind of change need?

The number of reviewers should depend on what a change affects, not who wrote it. Anthropic's Claude Code team uses three tiers: zero human approvals for low-risk changes once automated checks pass, one for ordinary changes and two for high-risk changes. A team of three to six developers can adapt this approach using a clear list of sensitive code paths and repository approval rules, without building an automated risk classifier.

This is not simply an AI-era idea. DORA's research on streamlining change approval found that effective approval combines peer review during development with automated checks. Heavyweight approval by people outside the team was associated with worse software delivery performance, without evidence that it reduced change failure rates. Risk-based tiers allow a small team to retain meaningful peer review without making every change wait for the same level of approval.

Four rules make the system work.

Keep the high-risk tier small. Two reviewers on a team of four means half the team must participate in each high-risk review. On a team of three, it means almost everyone is involved. Reserve this tier for changes where mistakes could expose sensitive data, lose money or disrupt production, such as authentication, payments, migrations, infrastructure, CI configuration and public APIs. If more than one in five pull requests falls into this tier, review whether the list is too broad. For a three-person team, one reviewer plus a documented rollback plan may be more practical than requiring two reviewers for every high-risk change.

Make the low-risk tier genuinely safe. Zero human approvals only makes sense when automated checks can catch likely failures and the change can be reversed safely. A wording change on an ordinary marketing page might qualify. A change to a legal notice, payment logic or security warning may not, even if it looks like a small text edit.

Never approve your own work. For the medium tier, an eligible teammate must review the change. The low tier is different: the author can merge because the team has agreed that passing automated checks is sufficient for that category. The distinction should be explicit in the team's rules.

A bot does not fill a human approval slot. Anthropic's approach does not count an agent's approval as a person's approval. GitHub's ruleset documentation also explains how approvals are handled when Copilot opens a pull request that is not attributed to a person. GitLab's handbook takes a stricter position: nobody may approve a change they wrote or directed an agent to write. For a small team, use Anthropic's tiered approval model, but consider GitLab's stricter rule for high-risk changes involving AI-generated code.

Wiring it into GitHub or GitLab

You do not need a sophisticated classifier to get started. You need a list of sensitive code paths, clear approval requirements and repository settings that support the rules.

1. Create a CODEOWNERS file. On GitHub, a CODEOWNERS file identifies the people responsible for particular files or directories. GitHub can automatically request reviews from those owners when a pull request changes their code, and repository settings can require their approval before merging.

List the high-risk paths first, for example:

• /src/auth/
• /src/billing/
• /migrations/
• /infra/
• /.github/workflows/

Assign the appropriate owners to each path. GitHub applies the last matching pattern when multiple patterns match a file, so put broad patterns before more specific ones. Make sure every sensitive path has an owner who is available to review it.

2. Set approval requirements to match the risk tiers. GitHub rulesets include a required reviewers rule that can apply when a pull request changes specified files or directories. The required number of approvals can be set from zero to ten. Where your repository and plan support the necessary configuration, use two approvals for high-risk paths and one for ordinary product code. Low-risk changes can proceed with no human approval once their required automated checks pass.

Check the feature and plan requirements before implementing this setup. The path-specific required reviewers rule requires an organisation with teams and is not available on user-owned repositories.

3. Prevent old approvals from becoming stale. A reviewer may approve a pull request, then the author may push more changes that materially alter the code. The earlier approval should not automatically count as approval of the new version.

GitHub's protected branch settings can dismiss stale approvals when new commits change the diff. They can also require the most recent reviewable push to be approved by someone other than the person who pushed it. These settings help ensure that the version being merged is the version that was reviewed.

Check the plan requirements as well: protected branches on private repositories require GitHub Pro, Team or Enterprise. Include that cost in your decision if the workflow depends on these protections.

4. Keep the main branch green. Anthropic uses a merge queue to test each pull request against the latest version of the target branch and other pull requests already in the queue. This helps catch conflicts or failures that appear when changes are combined.

GitHub's merge queue feature is available for private repositories on GitHub Enterprise Cloud. A small team on a lower-cost plan can instead require branches to be up to date before merging, then rerun checks against the latest target branch. This offers an alternative way to guard against merging changes that no longer work with the current code, although it can require authors to update their branches and run checks again.

On GitLab, approval rules define how many approvals a merge request needs and who can provide them. Code Owners sections can also require a specified number of approvals for particular parts of the codebase. These capabilities are plan-dependent, with the relevant approval rules and Code Owners features requiring Premium or Ultimate.

The objective is not to copy every setting used by a much larger company. It is to make your own team's rules explicit and enforceable. Start with a short list of high-risk paths, require the right number of human approvals, and make automated checks mandatory. Once the process is working, use actual review delays, failed changes and reverts to decide what needs tightening or simplifying.

What turnaround should you promise each other?

Promise to give every pull request a first human response within one working day, and during shared working hours whenever possible. Google's published guidance sets one business day as the maximum time to respond to a review request. For teammates in different time zones, it recommends responding while the author is still working or before they start work the next day.

Google's speed of code reviews guidance states: "One business day is the maximum time it should take to respond to a code review request (i.e., first thing the next morning)." For different time zones, reviewers should try to respond before the author's working day ends, or finish the review before the author starts work again.

GitLab, an all-remote company, sets a looser review-response service-level objective (SLO): team members should respond in under two business days. If a reviewer cannot meet that target, GitLab asks them to say so within 36 hours.

A response does not have to mean approval. It can be an approval, a question, or a request to split an oversized pull request into smaller pieces. The point is to stop teammates from waiting in silence.

The overlap-hours rule

Set your response promise according to how many working hours the author and reviewer share. The following is a practical template based on the research and guidance above, not a measured rule for every small team.

• Four or more shared hours. Respond during the overlap on the same day. Aim to merge most routine pull requests on the day they open, as you might in an office where everyone works together.
• One to three shared hours. Reviewers should clear their queues during the first hour of their working day, which may be the end of the author's day. Authors should open pull requests before the overlap begins so the first review can happen while both people are online.
• No shared hours. Treat the review as a handover rather than a live conversation. The author should explain what changed, what needs checking and what can wait. The reviewer should leave clear comments and approve when the remaining issues are minor, allowing the author to fix small things without waiting for another review round.

Google's guidance explicitly allows a reviewer to give "LGTM/Approval even though they are also leaving unresolved comments" when the remaining points are minor. It says this is especially worth considering when the developer and reviewer work in different time zones and would otherwise have to wait a whole day. LGTM means "looks good to me."

For a distributed team, approving with minor comments can eliminate an entire round of waiting. The reviewer can approve the overall change while leaving suggestions that the author can address separately, provided those comments do not identify problems serious enough to block the change.

Keeping reviews focused also helps. Sean Goedecke argues in a post on code review mistakes that a review should not contain more than five or six comments. Every optional nitpick can create another day of delay when teammates are separated by an eight-hour time difference. Google's standard of code review follows a similar principle: approve once a change "definitely improves the overall code health of the system", even if it is not perfect.

Research on overlapping work hours produces a surprising result. In a randomised laboratory experiment involving 42 two-person teams, Espinosa, Nan and Carmel found that a small time-zone separation significantly slowed production, but speed increased again when there was no overlap. This created a U-shaped relationship between time separation and speed. However, accuracy declined as separation increased.

The finding suggests that partial overlap can be particularly difficult when teams have not agreed on how to use it. A team with no overlap can still work quickly if it has a reliable handover process. The experiment involved a map exercise rather than code reviews, so it illustrates a possible mechanism rather than measuring how your team will perform. Research on follow-the-sun work reaches a similar conclusion: handover efficiency matters.

Remind people, automatically

A pull request that has been waiting too long should automatically remind its reviewer. This prevents authors from having to chase colleagues manually and makes the review process more reliable.

Two large experiments found that reminders can help. In a randomised trial covering 147 Microsoft repositories, Maddila and colleagues found that reminders reduced pull request resolution time by 60% across 8,500 pull requests compared with overdue pull requests that received no reminders.

At Meta, a cluster-randomised experiment involving more than 30,000 engineers tested reminders for code changes that had received no action for 24 hours. The reminders reduced time spent in review by 6.8% and time to the first reviewer action by 9.9%. Meta also found that developers' perceptions of review speed were closely linked to changes that waited more than 24 hours.

That makes 24 hours a useful point at which to trigger a reminder. The exact timing can depend on your team's working hours, but the principle is simple: let the system follow up automatically instead of relying on people to remember.

When to pair instead, and when to merge behind a flag

If a pull request reaches a third round of comments, stop the back-and-forth and talk. A 20-minute call during shared working hours may resolve disagreements that would otherwise take another two days of messages. If the team has no overlapping hours, the author can record a short walkthrough explaining the change and answering likely questions.

For changes that are safe to release gradually, consider merging them behind a feature flag. A feature flag lets the team deploy code without immediately making the new functionality available to users. The team can then review and test the change before switching it on.

Anthropic includes additive changes behind a feature flag in its low-risk review tier for this reason. If the feature remains switched off, it is less likely to affect users immediately. However, the code still needs appropriate automated checks, and the team must review the decision to enable the feature. A flag reduces release risk; it does not make faulty code harmless.

The practical goal is to remove unnecessary waiting without weakening review quality. Agree on a first-response deadline, automate reminders, make handovers clear and switch to a conversation when written comments stop being efficient.

What does the first month of a code review process for a small team look like?

Set aside four weeks to build and test the new process. Start by measuring how long pull requests wait today. Then define your risk tiers, configure the repository rules, agree on response times and rotate reviewers. At the end of the month, compare the results with your starting measurements.

Change one thing at a time so you can identify which changes actually help.

Week one: measure the delays and write the rules

Start with your last 30 merged pull requests. For each one, record three measurements:

• How many hours passed before the first human response?
• How many rounds of review comments and revisions were needed?
• How many hours passed between approval and merging?

These measurements show where the team loses time. For example, a pull request might receive its first response quickly but then sit approved for another day before merging. That is a different problem from a pull request that waits two days for its first reviewer.

Anthropic says it tracks similar measures, including the median and 95th-percentile number of human approvals per pull request, human comments and how long pull requests wait for people. It also monitors reverts and incidents to check whether faster reviews are compromising quality.

Next, agree on your high-risk paths in a team meeting. These are parts of the code where a mistake could have serious consequences, such as authentication, billing, database migrations, infrastructure, CI configuration and public APIs. Write the list down so everyone knows which changes need extra review.

Week two: build the rules into your repository

Now configure your code repository to enforce the process you agreed on.

Add a CODEOWNERS file to identify who is responsible for reviewing sensitive parts of the code. Configure the required number of reviewers for each risk tier, and make sure the relevant automated checks must pass before merging.

Enable dismissal of stale approvals where supported. This means that if a developer makes significant new changes after approval, the old approval can be withdrawn so the updated code gets reviewed. Enable automatic merging when the required approvals and checks are complete, if your platform supports it and the team is comfortable with the setting.

Finally, write your turnaround rule into the repository's contributing guide. For example: every pull request should receive a first human response within one working day, preferably during shared working hours. This makes the expectation visible to existing team members and new hires.

Week three: spread the review workload

A review process will not work well if one senior developer still receives nearly every request. Set up a reviewer rotation or automatic assignment so that reviews are distributed across the team.

GitHub's team review assignment settings support round-robin assignment and load balancing. Load balancing considers how many outstanding reviews each team member already has, and the system can skip people whose status is set to Busy.

For a small team, load balancing can help prevent one person from carrying the entire queue. However, it does not replace expertise: high-risk changes still need reviewers who understand the affected systems. GitHub's private-repository support for these settings depends on the plan, so check your repository's available features.

Add an automatic reminder for any pull request that has received no action for 24 hours. You can use a feature offered by your code-hosting platform or a scheduled job. The point is to prompt the reviewer without forcing the author to chase them manually.

Week four: compare the results

At the end of the month, compare your new measurements with the baseline from week one.

If the time to first response and the time between approval and merging have fallen, while reverts and incidents have not increased, the process may be working. You can then relax one restriction at a time, such as moving a particular path from medium risk to low risk or allowing a trusted developer to use a lighter review process for qualifying changes.

If reverts or incidents increase, investigate what changed and tighten the relevant rules. Do not assume that every increase is caused by the review process, but treat it as a reason to investigate before relaxing safeguards further.

This is the basic improvement loop: measure, change, compare and adjust. Anthropic's advice captures the principle: "start strict and loosen with evidence".

What the numbers can tell you about a person

A month of data can also reveal patterns in individual developers' work, but interpret those patterns carefully.

If one developer's pull requests repeatedly need four or five rounds of review while comparable changes from teammates usually need one, investigate why. The cause might be a skills gap, insufficient understanding of the codebase, unclear requirements or a process that makes it difficult to get feedback early. It is worth having that conversation promptly, using specific examples rather than assumptions. Remote developers can struggle without anyone noticing until the delays become a pattern.

The reverse situation matters too. If the senior developer's own pull requests consistently wait the longest, other team members may not feel confident reviewing their work. That is a knowledge-sharing problem, not simply a workload problem. Rotating reviewers and helping other developers understand the system can reduce the dependency on one person.

This is also when founders may begin asking whether the team needs a dedicated engineering manager. The question becomes more relevant when coordination, feedback and workload distribution need sustained attention beyond what the developers can manage themselves. RocketDevs explores that decision in its guide to the first engineering manager at a startup.

Where RocketDevs fits

A second time zone can turn overnight review delays into a productive handover, but only if the review process is already working. With clear risk tiers, agreed response times and approval with minor comments, a reviewer can work through the queue while you sleep. You can start your day with reviewed or merged code instead of a new set of comments to address. Without those rules, the time-zone gap simply adds another day to every round of feedback.

RocketDevs developers work with 100% EU timezone overlap. For a team in the Americas, that can provide additional coverage: a developer can review code during your morning and hand it back by your afternoon, depending on the team's working hours.

There are two important boundaries. First, if your senior reviewer is already the bottleneck, hiring another developer will not automatically solve the problem. Fix review assignment and workload distribution first. Second, put every new developer, including a RocketDevs developer, on the medium-risk review tier for their first month. Trust should be earned through demonstrated understanding of the codebase, not assumed from experience or assessment results.

If you want to test whether adding development capacity across time zones shortens your review cycle, RocketDevs offers a 14-day risk-free trial, money back and 100% honoured. You can evaluate the effect using your own pull requests before making a longer-term commitment.

When you're ready to build a vetted development team, visit RocketDevs. Every developer completes 6-8 hours of assessment, and RocketDevs accepts the top 2% of candidates.

Conclusion: make code review a system, not a bottleneck

A small engineering team does not need more reviewers on every pull request. It needs the right review, from the right person, at the right time. Low-risk changes should move through automated checks with minimal friction. Everyday product work needs one capable reviewer. Changes that could compromise security, payments or production infrastructure deserve a second pair of eyes. And every pull request needs a clear path from opening to merging, without leaving developers waiting days for a response.

Start with four things: define your risk tiers, automate the rules, promise a first human response within one working day, and measure where the time goes. Then review the results after a month. If reviews are still slow, find the actual bottleneck before hiring your way around it. If quality slips, tighten the safeguards. The goal is not to approve code faster at any cost. It is to help good code reach production sooner without making the business less safe.

Time zones can become an advantage when reviews are treated as deliberate handovers rather than endless conversations. A developer finishing their day can leave work ready for a teammate to pick up, turning overnight waiting into progress. But that only works when ownership, expectations and risk are clear.

Build the review system before you scale the team.

When the process works, every additional developer has a better chance of increasing delivery capacity instead of adding another person to the queue. And when you need that extra capacity, RocketDevs can help you build a vetted development team, with every developer assessed for 6-8 hours and only the top 2% accepted. Start with a process that works, then add the people who help it work better.

Frequently asked questions

How many reviewers should a pull request have?

Most pull requests need one reviewer. Low-risk changes can need zero human reviewers if automated checks fully verify them and merging is safe. High-risk changes, such as those affecting payments, authentication or infrastructure, should have two reviewers. This follows the three-tier approach Anthropic's Claude Code team published in October 2026. Choose the review level based on what the change affects, not who wrote it. On a team of three, one reviewer plus a documented rollback plan can be a practical alternative for some high-risk changes.

How long should a code review take?

A pull request should receive its first human response within one working day. Google's published guidance sets this as the maximum time to respond to a review request. On a distributed team, aim to respond during shared working hours. If only minor issues remain, a reviewer can approve the overall change while leaving comments for the author to address, provided those issues do not need to block merging. The time spent waiting for a reviewer can matter more than the time spent reading the code.

Should the founder review every pull request?

No. Once a team has more than two developers, the founder should not automatically review every change. Doing so can make the founder a bottleneck that slows down the entire team. Research on open-source projects found that problems with reviewer assignment were associated with reviews taking 12 days longer to approve. The founder should review high-risk changes in areas they genuinely own, while a reviewer rotation handles routine work.

Can automated or AI review replace a human reviewer on a small team?

Automated tools can check code, run tests and identify potential problems, but they should not replace human judgement on important changes. Anthropic's approach allows low-risk changes to proceed without human approval when the required automated checks pass, but an AI agent's approval does not count as a human approval. GitHub and GitLab also have rules governing human approval of AI-generated changes. Use automation to catch problems early, while keeping human reviewers involved in changes above the low-risk tier.

How can a small remote team speed up code reviews without sacrificing quality?

Start by assigning review requirements according to risk, setting a one-working-day target for the first human response and automatically reminding reviewers when a pull request has been idle for 24 hours. Use reviewer rotation or load balancing to prevent one developer from carrying the queue. For teams in different time zones, clear handovers and approval with minor comments can reduce unnecessary waiting. Measure review times, reverts and incidents each month so you can improve speed without overlooking quality.

James Hitch, COO at RocketDevs.LinkedIn

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