As an engineering team grows from a handful of people to a dozen or more, code reviews start to change. What used to be a quick exchange between people who knew the entire codebase becomes inconsistent. One reviewer approves with a quick “LGTM,” another focuses on syntax details, while a third questions architectural decisions that not everyone even knew were on the table. Without a clear agreement on what a review should cover, the result is inconsistent quality and an exhausting process. A simple Code Review Checklist already solves a large part of this problem by aligning expectations from the start.
It creates a shared quality standard and helps spread knowledge that usually ends up concentrated in more senior engineers.
The cost of feedback without criteria
In a small team, ad-hoc reviews tend to work because context is shared. Everyone knows the product, the architecture, and the way code is written. As the team grows and the number of projects increases, that context stops being common. From that point on, unstructured reviews start to slow the flow and weigh on productivity.
The problems show up in familiar ways. Development timelines stretch because of unexpected rework cycles when a critical flaw is discovered too late. Senior engineers become overloaded by acting as the default guardians of quality, repeating the same advice about naming conventions or error handling in pull request after pull request. At the same time, newer team members struggle to get up to speed. They receive conflicting feedback from different reviewers or get their code approved without learning the standards and best practices that aren’t documented anywhere.
This goes beyond code quality. It’s a scaling problem that directly affects team speed and morale, turning reviews from a moment of collaboration into a slow and unpredictable queue.
From implicit knowledge to explicit standards
The goal of a structured approach is to make the team’s implicit knowledge explicit. A well-defined checklist isn’t about creating bureaucracy or policing pull requests. It exists to build a shared understanding of what “done” and “quality” mean for your team. This reduces the cognitive load for everyone involved.
For the person writing the code, the checklist works as a self-review guide even before asking for feedback, helping catch common issues early. For reviewers, it defines a clear scope, helping them focus on what matters instead of getting lost in minor details or feeling obligated to comment on everything. It shifts the conversation away from subjective style preferences toward objective, agreed-upon criteria that actually impact the health of the codebase.
Code Review Checklist
1. Understand the context of the change
Is the goal of the PR clear?
Is the change aligned with the ticket/issue?
Are all changes in the PR necessary and related to the problem?
2. Code quality
Does the code follow the style guide (lint)?
Is it readable and easy to understand?
- Are variable, method, and class names descriptive?
- Does the code structure make it easier to understand?
Does the code reuse existing functionality where possible?
Is there code duplication that could be avoided?
Can the logic be simplified without losing clarity?
Are there design patterns that could be applied here (e.g., Strategy, Factory)?
Does the implementation respect principles like SRP, OCP, DIP?
3. Software architecture
Does the change follow the project’s architectural patterns?
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 extensions or changes easily?
4. Tests
Are there tests covering the main code paths?
Are success cases, failure cases, and edge cases included?
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?
Has sensitive data been removed from the code and properly protected?
Does the code avoid vulnerabilities like SQL Injection, XSS, or CSRF?
Are authentication and authorization mechanisms correct?
Have new dependencies been checked for vulnerabilities?
6. Documentation
Do comments explain complex parts of the code?
Has the documentation been updated to reflect important changes?
Are there useful annotations like TODO or FIXME, and are they necessary or temporary?
7. Performance
Does the code use resources (CPU, memory, I/O) efficiently?
Have loops or intensive operations been optimized?
Are asynchronous operations used appropriately for long-running processes?
Can the code scale well as the system grows or data volume increases?
8. Logging and Monitoring
Have logs been added where needed?
Are log messages clear, concise, and do they provide the necessary context?
Is sensitive data protected in logs?
Do the changes include support for monitoring or performance metrics?
9. Compatibility
Does the code maintain backward compatibility with previous versions of the system?
Have connected APIs or modules been tested with the changes?
Does the code work well across different environments (local, staging, production)?
10. Deploy
Have the changes been tested in CI/CD pipelines?
Have environment variables been documented and configured correctly?
Is there a clear rollback plan in case something goes wrong?
11. Implementation
Is this the simplest possible solution to solve the problem?
Are there unnecessary dependencies added to the project?
Is the logic implemented in a way that makes future maintenance easier?
12. Logical Errors and Bugs
Is there any use case that could lead to logical errors?
Does the change account for invalid or unexpected inputs?
Are external events (such as network failures or third-party APIs) handled correctly?
This is a template you can use in your day-to-day work, but ideally you should build it together with the team. Set up a meeting to discuss what should go on the list and make sure everyone is aligned. When engineers help create the standards, the chances of adoption are much higher. And remember to treat it as a living document. As the team grows, the product matures, and the stack changes, the checklist should evolve too.
Making the checklist part of the workflow
Once you have a checklist, you need to integrate it into the day-to-day process. Include a link to it in your pull request template. Encourage authors to mention which checklist items are most relevant to the change in the PR description. Reviewers can use it to structure feedback, referencing specific points to give clear and actionable suggestions.
Use it as a teaching tool. When leaving a comment, you can link to the checklist item it refers to. This helps standardize feedback and reinforce the team’s shared principles. Finally, set up a simple feedback loop, such as a dedicated Slack channel or a recurring meeting, to discuss what’s working, what’s not, and how the checklist can be improved over time.
To wrap up
I hope this checklist helps you make your code reviews more consistent, faster, and truly useful for the team.
You don’t need to follow everything to the letter every time — but the more these points become habits, the better the code quality gets (and the lower the chance of headaches later on).
If you want to adapt this code review checklist for your team, go for it.
