Doing code reviews is essential to ensure code quality, but let’s be honest: it often turns into a bottleneck in the development workflow. The pressure for fast deliveries, combined with the increasing complexity of projects, makes the process slow and, in some cases, even frustrating.
With AI stepping in to generate code, things got faster—but not always better. A GitLab survey found that 47% of devs already use AI to write code, but a study from Stanford University pointed out that these AI assistants can produce less secure code. In other words, thorough reviews are still essential to avoid issues.
In this article, we’ll dive into how to make Code Review more efficient—from best practices for faster reviews to how AI automation can speed up the process without sacrificing quality. If you want to streamline your team’s workflow without compromising security and code standards, stick around!
How the Code Review Process Usually Works
A code review starts when a developer completes an implementation, tests it, and does a self-review before opening a Pull Request (PR). Then, they select reviewers who can provide meaningful feedback on the changes. Reviewers analyze the logic, readability, and whether the code follows team standards, leaving suggestions and corrections as needed. The author then adjusts the suggested points until everyone agrees. Once it’s fully approved, the code is merged into the main branch.
Some common challenges with this process:
- Vague or unhelpful feedback, making it harder to improve the code.
- Lack of time for thorough reviews, overloading the team.
- Slow reviews, creating bottlenecks and delaying releases.
- Superficial reviews, allowing critical errors to slip through.
Code Review in Practice for Authors and Reviewers
Here are some practical tips for both authors and reviewers to make code reviews smoother and more effective.
Best Practices for Code Authors
Review your own changes before requesting a review
Before sending your code for review, take a moment to analyze it carefully. Read your changes as if you were someone else reviewing them. Ask yourself: “Is it clear what I did? Is anything unnecessarily complex? Did I follow the team’s best practices?” This eliminates obvious issues and prevents unnecessary feedback loops.
Keep changes small and focused
No one likes opening a huge PR and trying to understand everything at once. The smaller and more focused your change, the faster you’ll get feedback. Small changes also reduce the risk of conflicts and make maintaining the codebase easier in the future.
Explain what changed and why
Don’t assume that reviewers will immediately understand everything. Write a clear PR description: what problem you solved, how you solved it, and if anything specific needs attention. If you had to make an important technical decision, explain why. A well-documented PR saves time and improves the review quality.
Test before submitting
Running tests before requesting a review isn’t optional—it’s essential. No one wants to waste time pointing out obvious bugs that could have been caught by simply running tests. If your change impacts a critical behavior, ensure tests cover that scenario. This gives the team more confidence to approve and merge your code.
Make it clear what you expect from the review
If there’s something specific you want feedback on—like an architectural decision, a performance concern, or a complex piece of logic—mention it in the PR description. Guiding the reviewers’ focus makes feedback more useful and prevents superficial reviews.
Best Practices for Reviewers
Start with the tests
Before diving into implementation details, pay close attention to the tests. They show which scenarios were considered and help understand the change’s impact. If the tests are well-written and cover the main cases, that’s a good sign. If they’re weak or missing, that should be one of the first discussion points.
Document important decisions
If significant discussions or changes arise during the review, document them. This can be done in PR comments or internal documentation, depending on complexity. Keeping track of decisions avoids repeating the same discussions in the future and helps maintain a quick reference history.
Explain the reasoning behind feedback
The worst thing for a developer receiving a review is vague comments like “This could be better” or “I think you can improve this”. If something needs to be changed, explain why and, if possible, suggest a solution. This makes reviews more productive and prevents unnecessary back-and-forth.
Avoid distractions and review with focus
Trying to review code while multitasking reduces feedback quality. Whenever possible, dedicate focused time to reviewing, without interruptions. This helps catch important details and avoids rushed or superficial approvals.
Prioritize what really matters
Not everything in the code needs to be discussed during the review. The focus should be on logic, security, readability, and the impact of the change. Small styling adjustments can be handled by linters and auto-formatters—there’s no need for lengthy discussions on minor details.
Use checklists to stay consistent
Even experienced reviewers can overlook things. Having a standardized checklist with key review points—like test coverage, security, best practices, and system impact—helps ensure consistent and efficient reviews.
👉 You can find more best practices here.
Why Automate Code Review with AI
Automated reviews are a smart way to make code review faster and more efficient. With AI-powered tools, code is analyzed based on the team’s standards, catching issues like security vulnerabilities, style violations, and missing tests. These tools provide detailed, accurate feedback, allowing many fixes to be made before a human even reviews the code.
Right now, around 35% of developers already use AI to generate code review summaries, helping to quickly spot critical issues and standardize the process. Instant feedback speeds up error correction and reduces rework, freeing up time for the team to focus on what really matters.
Data on the Impact of Automation
Studies show how AI is transforming the code review workflow. Here are some key examples:
- 90% reduction in review time – Teams using automated review tools saw their review time drop drastically. Tasks like style checks, common coding issues, and bug pattern detection were sped up, freeing up to 90% of the time previously spent on these activities.
- Fewer bugs in production – By catching more issues during review, AI helps prevent failures that would otherwise reach users. Some teams report 70% fewer post-deployment bugs after implementing automated checks, leading to more stable software and less rework.
- Up to 50% increase in developer productivity – With AI speeding up feedback cycles, developers spend less time waiting for reviews or fixing issues too late. This efficiency boost can increase team productivity by up to 50% since devs can focus on building new features instead of fixing problems.
How to Implement AI Code Reviews
1. Define your team’s standards
Before automating any part of the code review process, clearly define your team’s standards. Take time to document best practices, including style rules, security guidelines, and quality criteria. The clearer these standards are, the more effective automation will be in ensuring a consistent codebase without sacrificing quality.
2. Choose the Right Tool for Your Team
2. Choose the right tool for your team
The right tool makes all the difference in code review efficiency. At Kodus, we developed Kody, an AI built to automate code reviews and optimize your team’s time. Our goal isn’t to replace human review but to handle repetitive tasks, identify issues with precision, and ensure that code meets team standards.
With automation, you can:
Generate automatic, clear PR summaries
Gives developers a quick overview of proposed changes, saving time and reducing confusion.
Ensure Your Code Follows Team Standards
Kody analyzes your code based on the standards your team has already set, making sure best practices and custom formatting rules are always followed.
Catch Bugs Before They Reach Production
It scans code patterns and behaviors to prevent issues before they even happen.
Seamlessly Integrate Automation into Your Workflow
Tools like Kodus easily integrate with the platforms your team already uses, like GitHub, GitLab, and CI/CD pipelines. That means real-time feedback without disrupting your workflow.
Next Step: Automate Your Code Review with Kody
If your team is still spending too much time on manual code reviews, it’s time to try a smarter approach. With Kody, you can automate a big part of the review process, ensuring more consistency, speed, and quality in development.
Want to see it in action? Sign up on Kodus and try Kody in your workflow
