Over 60% of software development teams report that manual code reviews are slowing down their release cycles.

Even with bigger teams and faster coding tools, reviewing pull requests can still slow teams down. Comments pile up, small issues stick around, and reviewers often spend more time on repetitive checks than on their more important tasks.

That’s where GitHub code review automation comes in. It takes care of the routine checks; style, tests, and security, so reviewers can focus on what really matters.

Fast coding workflows are great, but smart reviews are what keep teams confident and ahead of schedule.

TLDR

• Manual PR reviews can slow down shipping

• Automating routine checks (style, tests, security) speeds up the review process

• AI tools can now catch subtle bugs and suggest fixes automatically

• Combining automation with human review gets you faster merges and better code

• Start small with linting and tests, then expand your automation gradually

What does automating PR reviews mean?

At its core, automating your review workflow is about letting software do routine checks for you. Instead of waiting for a human reviewer to catch small issues, automated systems scan every pull request for errors, style inconsistencies, security risks, and test failures.

Think of it as a first line of defense. Automated checks ensure that only meaningful issues make it to your human reviewers, reducing the time teams spend on repetitive tasks. And with AI code review tools, these systems can even suggest fixes, detect subtle bugs, and enforce best practices, all without slowing down the development workflow.

By automating parts of your review process, teams can:

• Shorten review cycles

• Catch more bugs early

• Keep code consistent across large projects

• Free human reviewers to focus on logic and design

In short, automating PR reviews turns code reviews from a bottleneck into a reliable, faster, and smarter part of your workflow.

How to automate PR reviews with AI code review in practice:

Modern development teams rely on GitHub code review automation to handle repetitive, time-consuming checks. Over 65% of software teams use automated checks or CI/CD pipelines as part of their review workflow.

Here's what typically runs under the hood:

1. Static analysis

Tools like ESLint, Pylint, Flake8, RuboCop, or Clang-Tidy check code for syntax issues, unused variables, unsafe operations, or common anti-patterns. These run automatically on every PR.

2. Automated tests

GitHub Actions or CI platforms (like CircleCI, Jenkins, or GitLab CI) run build steps and test suites: unit tests, integration tests, or snapshot tests to confirm nothing breaks when code changes.

3. Security and dependency checks

Automated scans review dependencies for known vulnerabilities, outdated packages, or risky libraries. Tools like Dependabot and Snyk can flag issues early, ensuring the codebase stays secure.

4. Code coverage and quality metrics

Automation tracks test coverage and highlights untested areas of the code. This helps teams maintain high-quality code standards and ensures critical paths are properly tested before merging.

5. Review integration and reporting

All these automated checks feed results directly into the pull request. Reviewers can see which checks passed, which failed, and any flagged warnings, allowing them to focus solely on design, architecture, and complex logic rather than repetitive verification.

By combining these layers, automating your PR review workflow ensures that only meaningful issues reach human reviewers. This reduces review time, catches errors early, and helps teams maintain consistent, reliable, and high-quality code.

Human vs automated review: What each handles best

Let’s break down the difference between AI code review vs human reviews to see how each approach handles typical pull request checks:

By automating these routine tasks, reviewers can focus on design, architecture, and edge-case scenarios, improving both speed and quality. Teams using automated code review workflows report 20-30% faster merge times and significantly fewer errors reaching production.

Benefits of automating your PR review workflow

Using automated PR reviews brings clear advantages for engineering teams, from speeding up reviews to improving overall code quality. Some verified insights:

• Faster review cycles: Teams using automated checks and CI/CD pipelines experience shorter merge times and quicker feedback loops. GitHub's State of the Octoverse 2024 highlights widespread adoption of CI/CD pipelines among active repos.
  
  

• Fewer errors in production: Automated checks catch style, linting, test, and basic logic issues early, preventing bugs from reaching production.
  
  

• Consistent code standards: Automation ensures coding standards are applied across all pull requests, especially helpful for large or distributed teams.
  
  

• Focus on meaningful feedback: Offloading repetitive tasks to automation or AI lets human reviewers focus on architecture, logic, and edge cases.
  
  

• Scales with the team: As projects grow, automation helps maintain consistency and manageability.

Together, these advantages explain why more teams are adopting automation in their GitHub review process, because it helps them ship dependable code faster, without adding extra burden on developers.

How to set up automated PR reviews on GitHub

Implementing automated PR reviews doesn't have to be complicated. With the right setup, teams can streamline reviews, reduce errors, and save valuable time. 

Here's a practical roadmap:

1. Set up automated checks

Most teams use GitHub Actions or an external CI service to run:

• Linting and formatting checks

• Unit tests, integration tests, or smoke tests

• Type checkers (TypeScript, mypy, etc.)

• Security scans

• Dependency vulnerability audits

These workflows ensure every pull request goes through the same baseline validation before a reviewer even opens it.

2. Integrate AI-powered review tools

AI code review tools can improve automation by:

• Suggesting fixes for common coding issues

• Detecting subtle bugs or bad patterns

• Learning from past pull requests to provide smarter feedback

3. Define reviewer roles and responsibilities

Even with automation, human reviewers are essential. Define clear roles:

• Automation handles routine checks

• Humans focus on logic, architecture, and edge cases

• Use automated summaries or flagged issues to prioritize reviewer attention

4. Track and adjust workflow performance

Teams typically monitor metrics like:

• Time to first review

• Time to merge

• Number of failed checks per PR category

• Recurring patterns flagged by automation

These metrics help refine the automation configuration over time.

5. Start with high-impact checks

Most engineering teams begin with linting and tests, then gradually introduce security scanning or AI review once the core workflow is stable. This prevents introducing too many signals at once.

Key takeaway: Combining automation and AI code review with human judgment allows teams to move faster, reduce errors, and focus on what really matters: writing reliable, high-quality code.

Common challenges when automating PR reviews

Even though automating your review workflow offers clear benefits, teams often face a few challenges when adopting it. Understanding these can help ensure a smooth implementation:

1. Resistance to change

Developers and reviewers may be used to manual processes. Shifting to automated or AI-assisted reviews can feel like a big change.

Solution: Start small, automate routine checks first, and show the time savings to the team.

2. Over-automation

Some teams try to automate everything at once, from style to security to performance. This can overwhelm the workflow and create false positives.

Solution: Focus on high-impact areas first, like formatting, tests, and basic security checks, before expanding automation.

3. Inconsistent rules and standards

If automation isn't properly configured, it may enforce rules inconsistently, frustrating developers.

Solution: Define clear guidelines and align automation with team coding standards. Regularly review and update rules as needed.

4. Balancing AI suggestions and human judgment

AI-assisted tools can suggest fixes, but reviewers still need to check logic, architecture, and edge cases. Blindly trusting automation can lead to mistakes.

Solution: Position automation as a helper, not a replacement. Encourage humans to focus on high-value feedback.

5. Integration with existing workflows

Adding automation to existing CI/CD or review workflows may require initial setup effort, including permissions and pipeline adjustments.

Solution: Plan integration carefully, test on smaller repositories, and document the process for the team.

Building better review workflows

Automating PR reviews works best when it handles repetitive checks – things like formatting, test validation, and basic security scans, while developers focus on the decisions thatrequire human judgment: architecture choices, logic validation, and edge case handling. 

When you choose the right AI code review tool automation does the busywork. Humans focus on logic, design, and edge cases. Together, they make reviews smarter, faster, and more effective.

cubic’s code quality tool adapts to your workflow, learns your team's habits, and frees reviewers to focus on what truly matters: writing better code, faster. It take a different approach than most automated code review tools. While other tools prioritize speed, it intentionally use more compute and reasoning to analyze your code thoroughly. This means cubic catches subtle bugs that require cross-file knowledge, something that faster tools often miss. Plus, cubic learns from your team's knowledge, adapting over time to your unique coding patterns.

Give it a try and watch it for yourself!

Sign up today or book a demo call to see how smarter automation can transform your GitHub workflow without adding complexity.