Code Review is crucial for maintaining code quality, but let’s be real: it often turns into a bottleneck in the dev workflow. The pressure to ship fast, combined with the increasing complexity of projects, makes the process slow and sometimes 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
Code review starts when a developer finishes their implementation, tests it, and does a self-review before opening a Pull Request (PR). Then, they pick reviewers who can provide relevant feedback on the changes. The reviewers check the logic, readability, and whether the code follows the team’s standards, leaving suggestions or corrections when needed. The author then makes the necessary adjustments until everyone is on the same page. Once the final approval is in, the code gets merged into the main branch.
Some Common Challenges in This Process:
- Unhelpful or vague feedback, making it harder to improve the code.
- Not enough time for proper reviews, leading to overworked teams.
- Slow reviews, creating bottlenecks and delaying releases.
- Superficial reviews, allowing bugs or bad practices 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 go through 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 helps catch obvious issues upfront and avoids unnecessary feedback.
Keep changes small and focused
Nobody likes opening a massive PR and trying to understand everything at once. The smaller and more focused your change is, the faster the feedback will be. Plus, smaller changes reduce the risk of merge conflicts and make long-term maintenance easier.
Explain what changed and why
Don’t assume reviewers will immediately understand everything. Write a clear PR description: What problem did you solve? How did you solve it? Is there anything specific they should pay attention to? If you made an important technical decision, explain your reasoning. A well-described PR saves time and improves the review quality.
Test before submitting
Running tests before a review isn’t optional—it’s a must. Nobody wants to waste time pointing out obvious bugs that could’ve been caught with a simple test run. If your change affects a critical part of the system, make sure there are tests covering that scenario. This gives the team more confidence in approving and merging your code.
Be clear about what you expect from the review
If there’s something specific in your code that you want extra attention on—like an architectural decision, a performance concern, or a tricky section—mention it in the PR description. Guiding reviewers helps them provide more useful feedback and avoids shallow reviews.
Best Practices for Code Reviewers
Start with the tests
Before diving into the implementation details, take a good look at the tests. They show which scenarios were considered and help you understand the impact of the change. If the tests are well-written and cover key cases, that’s a good sign. If they’re weak or missing, that should be one of the first things to address.
Document important decisions
If significant discussions or changes come up during the review, document them. This could be in PR comments or even in an internal doc, depending on the complexity. Keeping a record prevents the same questions from resurfacing later and helps maintain a quick-reference history of key decisions.
Explain the reasoning behind your feedback
The worst thing for a developer is receiving vague comments like “This could be better” or “I think you can improve this”. If something needs to be adjusted, explain why and, if possible, suggest a solution. This makes the review more productive and avoids unnecessary back-and-forth.
Stay focused—avoid distractions while reviewing
Trying to review code while multitasking can hurt the quality of your feedback. Whenever possible, set aside dedicated time for code reviews without interruptions. This helps catch important details and prevents rushed or superficial approvals.
Prioritize what actually matters
Not everything in the code needs to be debated in a review. The focus should be on logic, security, readability, and the overall impact of the change. Small style adjustments can be handled by linters and auto-formatters—no need for endless 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.
How to Implement AI Code Reviews
1. Define Your Team’s Standards
Before automating any part of the code review process, you need to be clear on the standards your team already follows. Take the time to map out and document best practices, including style rules, security guidelines, and quality criteria that should be checked in every review. The clearer these criteria are, the more effective the automation will be—ensuring consistency without sacrificing quality.
2. Choose the Right Tool for Your Team
Picking the right tool makes a huge difference in the efficiency of your code reviews. At Kodus, we built Kody, an AI designed to automate the review process and save your team’s time. The goal isn’t to replace human reviewers but to handle repetitive tasks, accurately spot issues, and ensure the code meets your team’s 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
