Identify Code Review Inefficiencies

Code review is a critical step in delivering high-quality software, but inefficiencies in reviewer assignment can slow down the process and create unnecessary bottlenecks. Common challenges include:

  • PRs sit unreviewed. When no clear owner is assigned, PRs can languish in the queue, delaying merges and frustrating developers.
  • The wrong people review PRs. If a reviewer lacks familiarity with the code, they might request unnecessary changes or miss critical issues, leading to rework and delays.
  • Review fatigue for senior engineers. A small group of senior engineers often handles most reviews, leading to burnout while junior engineers miss learning opportunities.
  • Knowledge silos and bus factor. Code knowledge is not effectively distributed, making it difficult for teams to operate efficiently when key engineers are unavailable.

Deploy Code Review Automations

To solve these code review challenges, elite Developer Experience teams are turning to automation. By automating the reviewer assignment process, you can orchestrate PRs to reach the right people quickly while balancing workload distributions.

Ensure PRs are assigned to the right experts

  • Assign Code Experts: Identifies and assigns reviewers who have previously worked on the code being changed, ensuring deep familiarity and reducing unnecessary feedback loops. Learn more.
  • Assign Reviewers by Directory: Routes PRs based on file paths, making sure they land with the right team or domain expert. This prevents misrouted reviews and ensures expertise-driven feedback. Learn more.
  • Notify Watchlist: Enables teams to set up a watchlist for specific files or directories, notifying the right people when a PR needs review. Learn more.
  • Review Sensitive Files: Enforces security and compliance by requiring specific reviewers for high-risk areas of the codebase. Learn more.
manifest:
 version: 1.0

# === Editable reviewer settings ===
orgName: your-org

sensitive_paths:
 - "src/app/auth/"
 - "src/app/routing/"
 - "src/app/resources/"

watchers:
 - owner: "juliaspencer"
   files:
     - "src/auth/"
 - team: "release"
   files:
     - "package.json"
     - "yarn.lock"
 - team: "infra"
   files:
     - "terraform/"
     - "helm/"

automations:
 # 1. Assign Code Experts
 assign_code_experts:
   if:
     - {{ pr.labels | match(term='suggest-reviewer') | some }}
   run:
     - action: add-reviewers@v1
       args:
         reviewers: {{ repo | codeExperts(gt=10) }}
     - action: explain-code-experts@v1
       args:
         gt: 10

 # 2. Assign Reviewers by Directory (UI)
 assign_ui_reviewers:
   if:
     - {{ files | match(regex=r/src\/ui\/.*/) | some }}
   run:
     - action: add-reviewers@v1
       args:
         reviewers: ["ui-team", "frontend-lead"]
     - action: add-comment@v1
       args:
         comment: |
           The following reviewers have been added because this PR modifies files in `src/ui/`:
           ui-team, frontend-lead

 # 3. Notify Watchers
 {% for item in watchers %}
 notify_watcher_{{ item.owner if item.owner else item.team }}:
   if:
     - {{ files | match(list=item.files) | some }}
   run:
     - action: add-comment@v1
       args:
         comment: |
           @{{ item.owner if item.owner else (["{{ orgName }} - ", item.team] | join) }} - this PR has changes you are watching:

           Files:
           {%- for file in files | filter(list=item.files) %}
           - {{ file }}
           {%- endfor %}
     - action: add-reviewers@v1
       args:
         reviewers: {{ item.owner }}
         team_reviewers: {{ item.team }}
 {% endfor %}

 # 4. Review Sensitive Files
 review_sensitive_files:
   if:
     - {{ files | match(list=sensitive_paths) | some }}
   run:
     - action: add-reviewers@v1
       args:
         reviewers: ["security-team"]
     - action: set-required-approvals@v1
       args:
         approvals: 2
     - action: add-comment@v1
       args:
         comment: |
           ⚠️ Sensitive files modified. Security review required.

           Files triggering review:
           {%- for file in files | filter(list=sensitive_paths) %}
           - {{ file }}
           {%- endfor %}

LinearB can automatically assign code experts based on past editors of the PR’s files. You can further assign reviewers by directory, use a custom watchlist, or configure rules to review sensitive files, routing PRs to the appropriate subject-matter experts quickly. This avoids misrouted reviews.

Encourage broader participation and knowledge sharing

  • Knowledge Share: Routes PRs to less-experienced engineers when possible, providing an opportunity for learning while ensuring quality through additional reviews. Learn more.
  • Welcome Newcomer: Posts a welcome message when a contributor makes their first PR, encouraging engagement and best practices. Learn more.
  • Automatic Slack Messages: Posts positive recognition in Slack for well-structured PRs, encouraging participation and reinforcing best practices. Learn more.

To avoid burnout and siloed expertise, deliberately route some reviews to less experienced engineers or different team members. By setting an expertise threshold, you ensure the reviewer is familiar enough with the area to contribute, but isn’t the person who would normally always get the PR. This spreads knowledge of that code to more team members, reducing bus risk. Less seasoned developers get to work through the code and learn, while frequent reviewers get a break. Over time, this rotation grows the collective code ownership on the team. No more “one senior dev is the only one who knows this module.”

Alt/Caption: Automated snapshots of expertise help distribute codebase expertise and reduce organizational risk. It also gives contributors a rare glimpse into the specific sections of the code they're most familiar with, driving ownership.

To encourage best practices, you can auto-post welcome messages for first-time contributors to guide them on next steps. This sets a positive, consistent tone that can scale. You can even set up automatic Slack recognition so that when a PR meets certain standards (small, well-documented, passes all checks), LinearB can post a kudos message in your team’s channel to celebrate it. This kind of reinforcement encourages developers to follow good practices consistently.

manifest:
 version: 1.0


# === Slack Settings ===
slack_webhook_url: "{{ env.SLACK_WEBHOOK }}"


automations:
 # 1. Welcome Newcomer
 welcome_newcomer:
   if:
     # Check if this is the first PR for a contributor (account age < 1 day)
     - {{ repo.author_age < 1 and repo.age > 0 }}
   run:
     - action: add-reviewers@v1
       args:
         reviewers: ["my_organization/mentors"]
     - action: add-label@v1
       args:
         label: 'new-contributor'
         color: '#FBBD10'
     - action: add-comment@v1
       args:
         comment: |
           Hello {{ pr.author }}! 🎉 Thanks for making your first PR, and welcome!
           Our mentors will review this PR and help guide you through next steps.


 # 2. Share Knowledge
 share_knowledge:
   if:
     # Triggered when share-knowledge label is applied
     - {{ pr.labels | match(term='share-knowledge') | some }}
   run:
     - action: add-reviewers@v1
       args:
         reviewers: {{ repo | codeExperts(gt=30, lt=60) | random }}
     - action: add-comment@v1
       args:
         comment: |
           We have assigned a familiar reviewer to help broaden expertise across the team.


 # 3. Automatic Slack Recognition
 slack_auto_recognition:
   if:
     # Require at least one test file modified (edit pattern if needed)
     - {{ files | match(regex=r/(test|spec)/) | some }}


     # Encourage small PRs: limit to 5 files
     - {{ files | length <= 5 }}


     # Require branch name to reference a Jira ticket (edit to match your naming)
     - {{ branch.name | includes(regex=r/[A-Z]{2,}-\d+.*/) }}


     # Limit PR size: 150 or fewer lines changed
     - {{ branch.diff.size <= 150 }}
   run:
     - action: send-slack-message@v1
       args:
         webhook_url: "{{ slack_webhook_url }}"
         message: |
           :tada: Kudos to {{ pr.author }} for submitting an exemplary PR: '{{ pr.title }}'!
           Well-structured, tests included, small and focused. Keep it up!
           PR: https://github.com/{{ repo.owner }}/{{ repo.name }}/pull/{{ pr.number }}

All of these automations provide consistency and structure. The team no longer has to rely on memory or heroics to maintain quality, the system quietly takes care of it in the background, and developers get guided, not nagged, into doing the right thing. Once this foundational practice is established, you can further streamline your team’s process with PR Labeling and even implement critical safeguards against security vulnerabilities, performance regressions, and compliance risks with PR Policy Setting.

Improve your overall cycle time by reducing the initial pickup time. And PR labeling is only the start! You can deploy Slack notifications to bring actionable alerts directly to developers, no context switching needed.

Track Code Review Impact with Key Metrics

To measure the impact of automated reviewer assignment, the LinearB platform connects code review practices to developer productivity outcomes. Monitoring these metrics over time gives you concrete evidence of the efficiency gains your automations are driving.

Merge code more efficiently by improving cycle time

Cycle time reflects how quickly teams deliver code. Two key drivers, pickup time and review time, are directly improved by automating reviewer assignments. Pickup times drop significantly because reviewers are automatically notified of the PRs that require their attention, reducing idle time in the review queue. Familiar reviewers require less time to understand the changes and can provide more targeted feedback, accelerating the review time. As pickup and review times decrease, the overall cycle time shrinks, helping teams deliver software faster while maintaining quality.

Encourage Deeper Code Reviews

Review depth measures the thoroughness of feedback, which is critical for catching problems before deploying to production. Assigning knowledgeable reviewers means more relevant, detailed feedback where it matters most.

At the same time, automations like Knowledge Share broaden participation by opportunistically routing PRs to newer team members, ensuring diverse input while balancing senior oversight. Ultimately, tracking review depth alongside cycle time provides a balanced view of both speed and rigor in the code review process.

Leverage Code Experts to Improve Code Quality

Change failure rate (CFR) tracks how often deployments fail. Reducing CFR depends on consistently catching risky changes before they hit production. Automated reviewer assignment ensures that code experts review sensitive PRs every time. Additional policies, like mandatory reviews for security-critical files, add extra protection. Combined with broader knowledge sharing, this leads to fewer defects and a measurable drop in CFR. As your CFR decreases, you can directly attribute this improvement to a more consistent, expert-driven review process that automation helps facilitate. 

The outcome is a virtuous cycle: faster reviews and merges, higher code quality, and a team that’s happier and more engaged. From a business perspective, this translates into measurable ROI: more feature delivery, fewer production problems, and optimized use of your engineers’ time.

With the metrics above, you can confidently advocate for these practices within your organization. You have the talking points and the metrics to back them up. This kind of data-driven narrative resonates with both technical and business stakeholders. It shows that DevEx improvements aren’t just feel-good initiatives; they directly support the company’s goals by improving efficiency, quality, and team performance.