If you’ve worked with software development, you know a good code review is about more than just catching bugs, it’s about driving quality, consistency, and growth within the team. But how can you make sure the process stays effective without becoming a bottleneck? And more importantly, how can AI help automate reviews, boosting efficiency without sacrificing quality?
In this article, we’ll dive into the best practices for doing efficient code reviews and explore how AI-powered automation can simplify the process, speeding up workflows while keeping your code aligned with project standards.
Why Do Code Review? (And What’s in It for You?)
You might be wondering, “Is it really worth spending time on code reviews?” The short answer: Absolutely! Code reviews go beyond just catching bugs. Here are some reasons why they’re essential for any engineering team’s success:
- Ensure Quality and Maintainability: Reviewers ensure the code follows team standards, is easy to understand, and is ready for future maintenance, which prevents rework and keeps the system solid. Reviewing a teammate’s code also confirms that business logic is correct, reducing the chances of nasty surprises in production.
- Knowledge Sharing and a Broader View: By using pull requests (PRs), developers not directly involved in a feature or fix get to see what’s happening in the project. This fosters a more cohesive team and builds awareness. Junior devs also benefit from seeing different ways to solve problems, accelerating their learning.
- Continuous Learning: Code reviews are a two-way street. As you review others’ work, you pick up new techniques and practices. Likewise, having your code reviewed gives you constructive feedback to grow as a developer.
Over time, code reviews help build stronger codebases, tighter teams, and a culture of continuous improvement.
Step-by-Step Code Review Process
Whether you’re new to code reviews or want to step up your game, here’s a simple guide to help you be more effective and impactful during the process:
- Take Your Time and Focus: Don’t rush through someone’s code. Set aside enough time to carefully read and understand what’s been implemented before you start looking for issues.
- Understand the Context: Don’t review code in a vacuum. Check the pull request description or task details to get the context. Knowing the “why” helps you better understand the “how.”
- Avoid Micromanaging: Focus on significant issues like security flaws, logic problems, and team standard violations. Skip nitpicking over formatting, let linters and auto-formatters handle those details.
- Be Kind and Respectful: A code review isn’t about pointing fingers. Treat it as a conversation between teammates, with the goal of learning and improving together. Good feedback should aim to teach and correct.
The goal is to ensure the code is understandable, maintainable, and, of course, works as intended.
Tips to Master Code Reviews
Now that you know the basics, here are some valuable tips to take your code review skills to the next level:
- Break Down Large Reviews: It’s easy to miss important details when reviewing big PRs. Break the process into smaller tasks, start with tests, move to business logic, and finish with code design.
- Use a Checklist: Having a checklist helps you cover all critical points. Include items like coding standards, logic, test coverage, and maintainability.
- Don’t Be Afraid to Ask Questions: If something doesn’t make sense, ask the author. What’s clear to them might not be obvious to others.
- Celebrate the Good Stuff: Code reviews aren’t just about finding mistakes—highlight good practices, creative refactors, or elegant solutions. Positive feedback motivates the team and creates a more encouraging environment.
- Try Asynchronous Pairing: Treat code reviews like asynchronous pair programming. Instead of just leaving comments, try to understand the code as if you were working on it side-by-side with the author.
How Big Companies Handle Code Review
Let’s look at how tech giants like AWS and Google approach code reviews to get some practical insights.
AWS: Standardized Code and Peer Reviews
AWS follows simple but effective rules to maintain code quality. Some of their best practices include:
- Code Standardization: All developers adhere to the same coding standards to avoid rework and make maintenance easier. Tools like linters enforce these standards, flagging formatting issues and bad practices.
- Peer Reviews: Every PR requires at least one reviewer, ensuring there’s always a second opinion from someone with relevant context.
- Smart Automation: Automated tools handle minor issues like formatting and simple rule validation, freeing reviewers to focus on business logic, design, and scalability.
AWS also tracks metrics like RTTM (Review Time to Merge) and Change Failure Rate to monitor and improve their review process.
Google: Automating Code Review with Machine Learning
Developers can spend up to 35% of their time on code reviews. Google uses machine learning to streamline the process by suggesting code edits during reviews, saving time and reducing repetitive tasks.
Results include:
- 7.5% of review comments are automatically resolved with AI-suggested changes.
- This saves engineers thousands of hours annually, letting them focus on creative and complex tasks.
Despite automation, Google ensures human collaboration remains central by promoting:
- Deeper Discussions: AI handles smaller tasks, so developers can focus on meaningful discussions about architecture and logic.
- Complementary Reviews: AI supports the process without replacing human input.
- Continuous Feedback: AI suggestions spark conversations that encourage learning and knowledge sharing.
Automating Code Reviews with AI
Kodus offers a solution for automating code reviews with AI through Kody, our AI-powered agent. Every PR is reviewed directly in your version control platform (like GitHub or GitLab), just like a human reviewer would.
Kody analyzes the code, gives detailed feedback, and highlights potential issues or areas for improvement. The goal isn’t to replace human reviews but to provide an initial layer of review to maintain quality and speed up the process.
Key features include:
- Detection of Quality Issues and Best Practices: Kody ensures the code follows consistent standards by spotting inconsistencies and suggesting improvements.
- Customizable Feedback: You can configure Kody’s behavior per PR, limiting feedback to what’s most relevant.
- PR Exclusion by Tags or Scopes: PRs with specific tags can be excluded from review when not needed.
- Branch-specific Reviews: Kody can review branches beyond the main one, ensuring critical areas get covered.
- Action-Level Control: Configure Kody to focus only on critical issues or include suggestions for improvement based on your team’s needs.
With Kodus, your development flow stays smooth, quality remains high, and reviews become faster and more efficient without losing the human touch.
It’s About More Than Just Code
Code reviews are not just about finding bugs or following standards. They’re about building a culture of quality, learning, and teamwork. Whether you’re in a small team or a massive company like Google or AWS, code reviews benefit both the code and the people who write it.
Next time you review someone’s code, grab your checklist, take the time to understand the context, and treat it as an opportunity to improve—not just the code but also the team.
