Everyone’s been there: you open a huge PR, try to figure out what changed, drop some basic comments… and just hope you didn’t miss anything critical. Without a code review checklist, the whole thing turns into more of a ritual than a real improvement step. We pretend to review, the author pretends to fix, and the bug shows up in production two days later.
The good news? You can flip that script with one simple thing: a solid checklist.
Why use a Checklist for Code Review?
- Consistency: Everyone follows the same standard. The code gets more uniform, and easier to read.
- Time saver: Less rework. Bugs and bad practices get caught early.
- Continuous learning: Every review becomes a chance to grow technically, especially for those just getting started.
Code Review Checklist
1. Context
Is the PR’s purpose clear?
Does the change align with the ticket or issue?
Are all changes in the PR necessary and relevant to the problem?
2. Code Quality
Does the code follow the project’s style guide (lint)?
Is it readable and easy to understand?
- Are variable, method, and class names descriptive?
- Is the structure clear and logical?
Does the code reuse existing functionality where possible?
Is there duplicated code that could be eliminated?
Could the logic or structure be simplified?
Are there opportunities to apply appropriate design patterns (e.g., Strategy, Factory)?
Does the implementation follow design principles like SRP, OCP, and DIP?
3. Architecture
Does the change align with the project’s architectural standards?
Are the responsibilities of each module, class, or function clearly defined?
Is the code decoupled and modular, making future maintenance easier?
Does the architecture support future scalability and extensions?
4. Testing
Are there sufficient tests covering the main paths in the code?
Do they include success cases, failure cases, and edge cases?
Are the tests reliable, well-written, and isolated from external dependencies?
Is the business logic easy to test?
Do the tests reflect real-world scenarios?
5. Security
Are all user inputs validated and sanitized?
Are sensitive data properly removed or protected in the code?
Does the code avoid common vulnerabilities like SQL Injection, XSS, or CSRF?
Are authentication and authorization mechanisms implemented correctly?
Have new dependencies been checked for vulnerabilities?
6. Documentation
Do comments explain complex parts of the code?
Has the documentation been updated to reflect significant changes?
Are useful notes like TODO or FIXME included? If so, are they necessary and temporary?
7. Performance
Does the code use resources (CPU, memory, I/O) efficiently?
Have loops or intensive operations been optimized?
Are async operations used appropriately for long-running tasks?
Can the code scale effectively with increased system growth or data volume?
8. Logs and Monitoring
Have logs been added where necessary?
Are log messages clear, concise, and context-aware?
Are sensitive data protected in logs?
Do the changes include monitoring or performance metrics?
9. Compatibility
Does the code maintain backward compatibility with previous system versions?
Have APIs or connected modules been tested with the changes?
Does the code run smoothly in different environments (local, staging, production)?
10. Deployment
Have the changes been tested in CI/CD pipelines?
Are environment variables documented and configured correctly?
Is there a clear rollback plan in case of errors?
11. Implementation
Is the solution as simple as possible to solve the problem?
Are there unnecessary dependencies added to the project?
Is the logic implemented in a way that makes it easy to maintain in the future?
12. Logical Errors and Bugs
Are there any use cases that could lead to logical errors?
Does the change account for invalid or unexpected inputs?
Have external events (like network failures or third-party APIs) been handled properly?
Bonus: Automating Code Reviews with AI
Let’s be honest: manual code reviews work, but they’re not always the best use of the team’s time. Especially when the issues are repetitive, predictable, and… avoidable.
That’s where Kody comes in — the AI that reviews code like it’s part of your squad.
It spots problems, suggests improvements based on best practices, and keeps your project’s technical standards in check — all without making the team waste time reviewing random console.logs or messy spacing.
Getting started with Kody:
- Visit Kodus to learn how Kody integrates into your project.
- Check out the documentation for detailed setup instructions.
- Add Kody to your repository, configure it, and start automating your code reviews.
If you’re looking to improve your team’s productivity and deliver better software, Kody is the ideal partner for your development process.
