Code Review Practices That Scale

●TL;DR

Code review is the highest-leverage quality practice in software engineering... and the easiest to do poorly at scale. The pattern I see in growing teams: reviews become a bottleneck, PRs sit for 2-3 days, context switches kill productivity, and senior engineers spend 30-40% of their time reviewing instead of building. The fix isn't "review faster." It's automating what machines do better than humans (formatting, linting, type checking, test coverage) so human reviewers focus on what they're actually good at: architecture decisions, business logic correctness, and knowledge sharing. Teams that implement automated quality gates reduce review time from 45 minutes to 15 minutes per PR while catching more bugs... because the reviewer isn't fatigued from checking indentation.

Part of the Engineering Leadership: Founder to CTO ... a comprehensive guide to scaling engineering teams and practices.

●The Review Bottleneck

At 5 engineers, everyone reviews everyone's code. PRs get reviewed the same day. Knowledge spreads naturally.

At 20 engineers, review becomes a queue management problem. Senior engineers are the bottleneck... they're the only ones qualified to review certain areas. Junior engineers wait 2-3 days for review. PRs grow larger because "while I'm waiting, I'll add more changes." Large PRs take longer to review. The cycle reinforces itself.

I've measured this at 4 companies. The median PR cycle time (open to merge) grows from 4 hours at team size 5 to 52 hours at team size 20. That 48-hour increase isn't review time... it's wait time.

●The Automated Quality Gate

Before a human sees the PR, automated checks should validate everything a machine can catch.

What to Automate

# GitHub Actions: automated quality gate
name: PR Quality Gate
on: [pull_request]

jobs:
  quality-gate:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4

      - name: Format check
        run: npx prettier --check .

      - name: Lint
        run: npx eslint . --max-warnings 0

      - name: Type check
        run: npx tsc --noEmit

      - name: Tests with coverage
        run: npx jest --coverage --changedSince=main

      - name: Coverage threshold
        run: |
          COVERAGE=$(npx jest --coverage --changedSince=main --json | jq '.coverageMap | to_entries | map(.value.statementMap | length) | add')
          if [ "$COVERAGE" -lt 80 ]; then
            echo "Coverage on changed files below 80%"
            exit 1
          fi

      - name: PR size check
        run: |
          LINES=$(gh pr diff ${{ github.event.pull_request.number }} --stat | tail -1 | awk '{print $4+$6}')
          if [ "$LINES" -gt 400 ]; then
            gh pr comment ${{ github.event.pull_request.number }} --body "This PR has $LINES changed lines. Consider splitting into smaller PRs for faster review."
          fi

The goal: by the time a human reviewer opens the PR, they know formatting is correct, types check, tests pass, and there are no known security issues. They can focus entirely on logic, architecture, and design.

●What Human Reviewers Should Focus On

With automation handling the mechanical checks, human review time is best spent on four areas:

1. Architecture and Design Decisions

• Does this approach fit the existing architecture?
• Will this scale to 10x the current load?
• Are there simpler ways to achieve the same result?
• Does this introduce unnecessary coupling between modules?

2. Business Logic Correctness

• Does the implementation match the requirements?
• Are edge cases handled (empty states, null values, concurrent access)?
• Are the error messages helpful to users?
• Is the behavior consistent with how similar features work?

3. Readability and Maintainability

• Could another engineer understand this code in 6 months?
• Are names descriptive enough without being verbose?
• Is the control flow easy to follow?
• Would a comment help clarify a non-obvious decision?

4. Knowledge Sharing

• Point out patterns the author might not know about
• Explain why an alternative approach would be better
• Link to internal documentation or past decisions
• Use the review as a teaching moment, not a gatekeeping exercise

●Review Protocol at Scale

Async-First Reviews

Synchronous review (sitting together, walking through code) doesn't scale past 10 engineers across time zones. Async review with clear conventions scales to hundreds.

The async review contract:

Ownership-Based Routing

Don't assign reviews randomly. Route them based on code ownership:

# CODEOWNERS file
# Core infrastructure
/src/lib/database/    @team-platform
/src/lib/auth/        @team-platform

# Feature areas
/src/app/dashboard/   @team-product
/src/app/billing/     @team-billing

# Shared components
/src/components/ui/   @team-frontend

CODEOWNERS ensures the reviewer has context on the code they're reviewing. A frontend engineer reviewing a database migration isn't providing useful feedback... they're rubber-stamping.

Small PRs by Default

The single most effective change for review speed: smaller PRs. The following are general guidelines based on patterns I've seen across teams ... your numbers will vary by codebase and team maturity:

Studies suggest that review quality drops significantly after 200-400 lines ... research from Microsoft (Czerwonka et al., "Code Reviews Do Not Find Bugs") found diminishing returns on reviewer attention past this threshold. Beyond 500 lines, reviewers tend to miss more defects than they catch as attention is exhausted.

How to enforce small PRs:

1. Stacked PRs. Break a feature into 3-5 sequential PRs that each build on the previous one.
2. Feature flags. Ship incomplete features behind flags. Each PR adds one piece of functionality.
3. Automated PR size warnings. The CI check mentioned above flags PRs over 400 lines.

●Review Anti-Patterns

The Rubber Stamp

"LGTM" without substantive feedback. This happens when reviewers are overloaded... they approve to clear their queue, not because they've reviewed the code.

Fix: Track approval time. If a PR is approved in under 3 minutes for a 200-line change, the reviewer didn't read it.

The Nitpick Review

30 comments about naming conventions and zero comments about the actual logic. Nitpick reviews are the leading cause of review resentment.

Fix: Automate style enforcement. If a comment could be a linter rule, it should be a linter rule.

The Architecture Astronaut

"You should rewrite this entire module while you're in here." Scope creep in reviews delays shipping without proportional quality improvement.

Fix: The "not in this PR" rule. If a suggestion isn't necessary for the current change to be production-ready, it goes in a follow-up ticket.

The Gatekeeper

One senior engineer who must approve every PR. Creates a single point of failure and a permanent bottleneck.

Fix: Define clear code ownership. Require approval from any member of the owning team, not a specific person. Two approvals from team members carry more confidence than one approval from a gatekeeper.

●Measuring Review Health

Track these metrics monthly:

The last metric is counterintuitive. Too few comments per 100 lines suggests rubber-stamping. Too many suggests nitpicking or inadequate automated checks.

●When to Apply This

• Your team is growing past 10 engineers and reviews are becoming a bottleneck
• Median PR cycle time exceeds 24 hours
• Senior engineers spend more than 25% of their time reviewing
• Review quality is inconsistent across the team

●When NOT to Apply This

• Team of 3-5 engineers where everyone reviews everything naturally
• Early-stage startup where shipping speed matters more than review process
• Solo developer (code review is a team practice)

Scaling your engineering team and code review is becoming a bottleneck? I help teams design review processes that improve quality while accelerating delivery.

• Technical Advisor for Startups ... Engineering team scaling guidance
• Next.js Development for SaaS ... Teams with built-in quality practices
• Technical Due Diligence ... Engineering process assessment

●Continue Reading

This post is part of the Engineering Leadership: Founder to CTO ... covering hiring, team scaling, technical strategy, and operational excellence.

More in This Series

• Technical Hiring Framework ... Hiring engineers who ship
• First Engineering Team Playbook ... Building your first 5 engineering hires
• Documentation That Engineers Actually Read ... Documentation as a scaling tool
• IC to Tech Lead ... The transition from building to leading

Related Guides

• AI Code Review: Catching What LLMs Miss ... AI-assisted review as a supplement to human review
• Technical Debt Strategy ... Managing quality at scale