Code reviews are one of the best ways to improve code quality and catch issues early on. They help teams identify bugs, logic errors, and design flaws before they turn into bigger problems.

In a 2020 Smartbear survey, respondents said that code reviews are the #1 way to improve code quality. Integrating code reviews into the development sprint saves time and money by reducing the cost of fixing defects later. It’s a simple but powerful practice that guarantees every feature is built on a strong foundation. Let's talk about some tips and best practices to help you implement or improve your code review process.

What is code review and why use it?

Code review, or peer code review, is a process in software development where one or more developers examine another developer's code. The goal is to identify issues, maintain compliance with coding standards, and encourage the exchange of techniques. 

The process often involves multiple roles—a moderator, peer developers, and quality engineers—who look at code complexity, potential bugs, and compliance with standards. Code review tools can help identify poor design patterns and bugs so that every new feature or product is built with high-quality code. 

Code reviews are a reliable way to catch bugs early in the development cycle. They minimize the risk of errors surfacing later when they are more costly to fix. Studies have shown that code reviews significantly improve code quality, and many consider them essential practice for maintaining high standards. By catching logic flaws, architecture concerns, and inefficiencies, teams can reduce testing time and improve their product. 

Code reviews also give team members the chance to learn from one another, introducing developers to different styles, techniques, and solutions. This knowledge exchange builds teamwork and helps everyone improve their skills.

The review process helps keep technical debt in check by making sure the code stays easy to maintain. It also prevents bad or inefficient code from piling up and improves security by catching potential vulnerabilities in advance.

Top 10 code review best practices

Use a checklist for every review

A code review checklist is a straightforward way to guarantee quality and consistency across projects. It helps reviewers evaluate code systematically and reduce errors. Here’s what to include in one:

1. Functionality: Does the code meet requirements? Are edge cases handled?

2. Readability: Is the code clean, well-commented, and easy to follow?

3. Performance: Are there any bottlenecks or inefficient loops?

4. Error handling: Are errors logged and managed properly?

5. Security: Are secure practices applied to avoid vulnerabilities?

6. Testing: Are all edge cases tested? Is the coverage adequate?

Introduce code review metrics to track and improve the process

Code review metrics offer valuable insights that help teams refine their development practices. By evaluating key aspects of the review process, teams can identify strengths, address inefficiencies, and stay focused on quality.

Here are some commonly-used metrics used to quantify the performance of review processes:

• Defect density: Defects found per line of code reviewed. 

• Review coverage: Percentage of code changes reviewed.

• Time to review: Average duration to complete reviews.

• Reviewer participation: Number of reviewers involved in each review.

Over time, analyzing trends lets teams refine processes and adopt better coding practices. Defect density numbers help improve software quality by catching issues early on. The time it takes to review data highlights inefficiencies, while participation metrics promote accountability.

Start by defining metrics aligned with team goals, like defect density or review coverage. Use tools like Git to collect reliable data, analyze results to pinpoint inefficiencies, and adapt processes. Lastly, continuously monitor progress to measure long-term improvements.

Limit reviews to 200–400 lines of code at a time

Limiting code reviews to 200-400 line segments at a time is a proven best practice for improving software quality and guaranteeing meaningful feedback. Research by SmartBear found that most bugs are detected within the first 200 lines of code (LoC), with detection rates dropping beyond this range. 

Visual from SmartBear

Submit pull requests that are small and focused

Small and focused pull requests (PRs) streamline the code review process, making it more effective for reviewers and developers. By limiting changes to manageable pieces, teams can improve cooperation, reduce errors, and enhance feedback cycles.

Smaller PRs are easier to review. With fewer changes to analyze, reviewers can provide more thoughtful and precise feedback without feeling overwhelmed. This also speeds up the review process, allowing developers to address comments quickly and merge changes sooner.

Focused PRs reduce the risk of bugs. When changes are scoped narrowly, it’s easier to identify and fix errors before they escalate. If something goes wrong post-merge, tracing the root cause is easier with smaller, isolated changes.

Smaller PRs improve collaboration. They encourage discussions about specific updates, helping teams share knowledge and align. By integrating smaller PRs into CI/CD pipelines, teams also benefit from faster builds and a smoother development process.

Agree on review deadlines upfront

Setting review deadlines upfront is key to timely feedback and project momentum. When teams agree on clear timelines, it fosters accountability and encourages collaboration, creating a more efficient code review process.

Timely feedback prevents bottlenecks by helping developers receive input quickly, allowing for prompt iterations and smoother progress. Defined deadlines also promote accountability, motivating reviewers to prioritize tasks and commit, which in turn keeps the work on schedule. 

To implement deadlines, teams should discuss review deadlines during project planning. Using tools like Jira or Trello to track deadlines gives visibility, and open communication accommodates unexpected changes. 

Give feedback that’s specific and easy to act on

Specific and clear comments help developers address problems efficiently, avoiding unnecessary back-and-forth. 

For example, instead of saying, “This needs work,” a code reviewer could specify, “Consider renaming x to userAge for clarity.”

Focusing on the code, not the author, ensures that feedback is constructive and professional. Highlighting vulnerabilities like nested loops or missing edge case handling with objective critiques keeps the conversation productive. Context is also necessary—explaining why a suggestion improves performance or readability helps everyone understand its value. 

Provide context to the reviewer using templates or comments

Giving context during code reviews helps reviewers understand the purpose and nuances of proposed changes. Structured templates or detailed comments help standardize and clarify communication.

Using pull request templates: Templates simplify the information shared in pull requests (PRs), improving communication within the team and making the code review process faster and more efficient. A well-designed PR template might include a description of the changes, the motivation behind them, details of testing, and links to related issues.

Adding inline comments: Developers can annotate complex or non-intuitive sections of code with comments, explaining logic or decisions. 

Facilitating cooperation: Contextual comments encourage reviewers to provide meaningful feedback, ask questions, and suggest improvements. 

Use static code analysis and automation to support reviews

Static code analysis and automation tools play a big role in improving the code review process. These tools automate routine checks, letting developers to focus on more nuanced aspects of their work. Static code analysis is good for:

• Early issue detection: Identify bugs and vulnerabilities before they escalate into larger problems.

• Consistency: Enforce coding standards across the entire project.

• Time savings: Automate repetitive checks, freeing reviewers for complex evaluations.

• Enhanced security: Spot potential risks early to strengthen the codebase.

• Broad coverage: Analyze extensive codebases efficiently and thoroughly.

Tools like Trag build on traditional static code analysis with AI-powered capabilities. Trag’s automated code review tool scans pull requests in real-time, offering context-aware feedback that helps identify and address issues more effectively. 

Unlike static tools with rigid rule sets, Trag allows for custom rules tailored to specific project needs. By integrating with platforms like GitHub and GitLab, it supports more adaptive and efficient code reviews while maintaining consistency and quality.

Ensure feedback justifies the stance and is constructive

Providing justified and constructive feedback is critical for building a positive, collaborative team environment. Thoughtful criticism improves code quality, motivates growth, and encourages open communication.

For example, if a developer used nested loops to process a large dataset, reviewers shouldn’t just say not to use nested loops. Instead, explain that they increase the time complexity to O(n²) and could cause performance issues with larger inputs. Suggest an alternative, like using a hash map, to make the process faster.

Clear, actionable feedback like this helps developers understand the problem and how to fix it, making the review process more effective.

Follow through by addressing all changes before merging

Make sure all feedback is addressed before merging code to maintain quality and avoid defects. This way, the code meets standards, improves reliability, and reduces the risk of bugs or inconsistencies. Resolving comments early in the process also keeps development on track and avoids problems down the line.

Better code reviews make better code

Code reviews are important for maintaining software quality and supporting team collaboration, but they can be time-consuming. Tools like Trag simplify this process with AI code review, automating error detection, enforcing standards, and speeding up your coding sprint.

By using automation and following these code review best practices, teams can save time and focus on improving their code while saving time and effort. Trag helps dev teams enhance their reviews and maintain consistent, high-quality results. Try AI code review with a free trial today.