Introduction

Code reviews are essential to ensuring high-quality software. They ensure that every change is functional, safe, maintainable, and in line with team standards. Reviews are crucial, but they often get in the way of releases, slowing them down, irritating developers, and leading to quality variation.

According to research, code reviews during development can reduce bugs by 36%. Additionally, they promote shared ownership and continuous improvement across the engineering organization.

In this comprehensive guide, you'll learn best practices and tips for conducting effective code reviews.

Key Takeaways

• Code reviews are about clarity. The goal is to ensure every change is clean, maintainable, and aligned with team standards.

• Defining a clear review workflow and shared guidelines eliminates confusion, speeds up reviews, and improves collaboration across teams.

• Constructive communication builds stronger teams. Feedback should educate and empower, not criticize. 

• A positive review culture helps developers learn and grow together.

• Automation removes the noise. Tools and CI pipelines should handle formatting, syntax, and testing checks so reviewers can focus on architecture, logic, and performance.

What is a Code Review?

A code review is the process in which other developers or a tech lead review your code before it’s added to the main project. The goal is to ensure the code is clean, reliable, and follows the best practices your team or company has agreed on.

During a code review, developers check whether the code works as expected and meets both functional and non-functional requirements. That means looking at how the code behaves, how efficient it is, and whether it’s easy to maintain, secure, and consistent with the rest of the project.

Let’s explore some of the most common hurdles that make code reviews less effective than they could be.

Must Read: How to Use AI for Code Reviews on GitHub?

Common Challenges in Code Reviews

Code reviews are straightforward in theory. You just check the code and then share your feedback. In practice, they can take a lot of time. Many teams struggle to ensure fairness. Here are some of the most common challenges teams face:

1. Slow Review Cycles

Reviews often pile up when developers are busy or when pull requests are too large. This causes delays, longer feedback loops, and slower releases.

2. Inconsistent Review Standards

Every reviewer has a different style. One might focus on formatting, while another dives into logic or performance. Without shared guidelines, feedback becomes uneven, and code quality depends on who’s reviewing that day.

3. Lack of Context

Reviewers sometimes get a pull request without enough background — no clear description, no linked ticket, or missing documentation. Without context, it’s hard to understand why changes were made.

4. Overly Nitpicky Feedback

Some reviews get stuck on minor style issues rather than focusing on the design or logic. This wastes time and makes developers angry because they feel like they are being watched too closely.

5. Large or Complex Pull Requests

Big PRs are harder to understand and review properly. Important issues get missed, or reviews drag on for days. Smaller, focused PRs are easier to review and merge.

6. Reviewer Fatigue

When developers are constantly asked to review large or repetitive code, they start losing focus. Fatigue leads to missed issues and lower-quality feedback.

7. Cultural or Communication Gaps

Sometimes, feedback feels personal or harsh, especially in distributed teams. Without a clear tone and intent, reviews can create tension rather than build collaboration.

8. Skipping Reviews Under Pressure

When deadlines are tight, teams often merge code without review. This might save time in the short term, but it increases technical debt and post-release bugs.

9. No Review Metrics or Visibility

Many teams don’t track review turnaround time, feedback quality, or participation. Without data, it’s hard to know whether your code review process is actually working.

The good news is that most of these challenges are fixable. By following proven best practices, teams can make code reviews faster, more consistent, and far more effective.

16 Best Practices for a Good Code Review

The code review process needs to be consistent, clear, and meant to help the whole team work better, not just find mistakes.

Here are some of the best practices that one should keep in mind when thinking about “how to do a good code review”:

1. Decide on a Clear Process

First, decide how your team will do reviews: through pull requests, pair reviews, or feature branches. Write down this process so everyone knows what to do and how to ensure the workflow stays the same from project to project.

2. Focus on the Right Things

Fixing typos and spacing isn't the point of a code review; the point is to ensure the code is easy to read, works well, and is reliable. Logic, maintainability, and security should come first. Let computers handle style issues so human reviewers can focus on providing helpful feedback.

3. Discuss the High-Level Approach Early

Before writing or reviewing thousands of lines of code, make sure everyone agrees on the design or architecture. Lightweight design reviews or short discussions can prevent major rewrites later.

4. Optimize for the Team

Each team does things in its own way. Figure out what "good enough" means for your work process. Find the right balance between speed and thoroughness. It doesn't help anyone if the perfect code ships late.

5. Default to Action

Avoid long PR standstills. If something is unclear, ask. If it’s fixable, fix it. Short, frequent review loops help teams maintain momentum and avoid bottlenecks.

6. Keep Pull Requests Small

Large PRs are hard to understand and easy to delay. Smaller PRs, ideally under 400 lines, are faster to review, easier to test, and less prone to missed defects.

7. Develop a Positive Feedback Culture

Reviews should build confidence, not fear. Use collaborative language, such as “Could we try…” or “What if we considered…,” instead of criticism. Positive feedback keeps discussions constructive and helps everyone grow.

8. Use Continuous Integration (CI) Effectively

Run automated tests and linting before or during the review. This reduces manual checks, prevents reviewer fatigue, and ensures every PR meets basic quality standards.

9. Delegate Nitpicking to Automation

Don’t waste human energy on style or syntax. Use linters, formatters, and AI platforms like Entelligence Deep Review to catch repetitive or low-priority issues automatically.

10. Communicate Clearly and Explicitly

Good reviews are straightforward to follow. Explain the why behind your comments, not just the what. Use simple tags like nit: (minor), suggest: (optional), or blocker: (must-fix) to make feedback easier to act on.

11. Use Explicit Review Requests

Assign reviewers deliberately rather than hoping someone will pick it up. Tools like Entelligence’s PR Dashboard can automatically route reviews to the right teammates based on expertise and availability.

12. Review Your Own Code First

Before requesting feedback, read your own changes as if you were the reviewer. Self-review catches simple errors and helps you write clearer, easier-to-review code.

13. Document as Much as Possible in Code

Good code explains itself. Use meaningful variable names and clear logic so readers don’t need extra context. Add comments only when you’re explaining why something was done, not what it does.

14. Write Clear Pull Request Descriptions

Every reviewer needs context. A good PR description should include what changed, why it changed, and how it was tested. This saves reviewers time and makes feedback more focused.

15. Keep Discussions Public

Whenever possible, keep review conversations in shared threads rather than private chats. It builds transparency, encourages shared learning, and helps others understand decisions later.

16. Use the Shared Repository Model

Work from a central repository with branches for each feature or fix. This gives everyone visibility into changes and simplifies collaboration.

Handwritten reviews can only do so much, even if the right culture and process are in place. This is where AI comes in. It helps teams focus on what's essential by automating repetitive checks.

Must Read: AI Code Review Techniques and Top Tools

How AI Improves the Code Review Process?

AI brings structure, speed, and clarity to every stage of the review process. It helps developers focus on meaningful work instead of manual checks.

Here’s how:

1. Automates Repetitive Checks

AI does the work reviewers have to do, like finding missing tests, pointing out syntax errors, and checking for style violations. Because of this automation, reviewers will spend less time fixing indentation or naming issues and more time reviewing the architecture and logic.

For example, AI-powered agents like Entelligence AI's Deep Review catch low-level issues instantly inside your IDE, helping teams maintain quality without adding overhead.

2. Provides Context-Aware Insights

AI doesn't just read code; it knows what it means. AI shows you what changed, why it matters, and where the risks might lie by analyzing your project structure, dependencies, and commit history. When reviewers see the bigger picture, they don't have to ask as many questions, such as "What does this function do?" or "Why was this changed?"

3. Speeds Up Review Cycles

With automated summaries, code analysis, and pre-review checks, AI significantly reduces the time required to review and approve pull requests. It prioritizes high-impact issues and eliminates unnecessary delays, helping teams ship faster while maintaining quality.

4. Improves Review Consistency

There are different kinds of human reviews. Some focus on logic, while others are more about style or performance. AI ensures standards are consistently met by requiring every pull request to follow the same rules, structure, and quality checks. This makes the process more fair and predictable, especially for large or spread-out teams.

5. Enhances Collaboration Across Roles

Code reviews are easier for everyone, from developers to engineering managers, thanks to AI-generated summaries and PR insights. Clear, plain-language updates let tech-savvy and non-tech-savvy stakeholders keep up with progress and help teams communicate more effectively.

6. Reduces Reviewer Fatigue

Reviewing multiple large PRs can be mentally draining. AI reduces this burden by filtering out noise and surfacing only what truly needs attention. It helps reviewers stay focused and maintain a high standard of feedback without burnout.

7. Links Code Reviews to Team Performance

AI links reviews to bigger measures of engineering performance. Sites like Entelligence AI connect review data to sprint progress, team velocity, and problems. Leaders can see how reviews affect delivery speed and quality, enabling them to make informed decisions without having to track everything by hand.

Once your review workflow is up and running, check whether it's actually working. By keeping an eye on the right metrics, you can see how fast, well, and how engaged your team is, which helps you keep getting better.

Must Read: Exploring PR Review AI Tools: Boost Code Quality Fast

Metrics to Measure Code Review Effectiveness

Below are the key metrics that every engineering team should track to evaluate the effectiveness of their code review process.

1. Review Turnaround Time (RTT)

What it measures:

The average time between when a pull request (PR) is opened and when it receives its first meaningful review comment.

Why it matters:

Slow feedback loops delay merges and frustrate developers waiting on reviews. A long RTT often signals overloaded reviewers, unclear ownership, or large, complex PRs.

How to improve it:

• Assign reviewers automatically using ownership rules or PR dashboards.

• Encourage smaller, focused PRs that are easier to review quickly.

• Prioritize review tasks during daily stand-ups to avoid pileups.

2. Time to Merge (TTM)

What it measures:

The total time from when a PR is opened to when it’s merged into the main branch.

Why it matters: 

TTM shows how well your code review process works as a whole, including review cycles, rework, and approvals. High TTM can slow releases and reduce team velocity.

How to improve it:

• Reduce back-and-forth by providing clear PR descriptions and context.

• Use automation to catch low-level issues before review.

• Keep reviews small and scoped to a single change.

3. Review Coverage

What it measures:

The percentage of merged PRs that at least one other developer has reviewed.

Why it matters:

Skipping reviews under tight deadlines increases technical debt and post-release defects. High coverage ensures every code change gets peer-reviewed before deployment.

How to improve it:

• Enforce review policies using CI pipelines.

• Rotate reviewers to distribute workload evenly.

• Make review completion a visible team metric.

4. Review Depth (Quality of Feedback)

What it measures:

The number of valuable comments per PR or per reviewer is often a good indicator of how thorough and helpful the reviews are.

Why it matters:

Too few comments can indicate “rubber-stamping.” Too many on trivial issues suggest reviewers are focusing on low-value details instead of logic and design.

How to improve it:

• Use checklists to guide reviewers to key areas such as security, performance, and test coverage.

• Automate style and formatting checks to keep focus on higher-level insights.

5. Rework Rate (Post-Review Commits)

What it measures:

The number of commits added to a PR after the initial review request.

Why it matters:

A high rework rate suggests unclear specifications, insufficient pre-review testing, or poor initial self-review. It also increases review cycles and slows delivery.

How to improve it:

• Encourage developers to review their own code before opening PRs.

• Add automated tests to catch early-stage issues.

• Clarify acceptance criteria and PR goals upfront.

6. Defect Escape Rate

What it measures:

The number of defects or incidents found after a PR has been merged.

Why it matters:

This is the most direct indicator of review effectiveness. If bugs frequently escape to production, reviews are missing key checks in logic, security, or performance.

How to improve it:

• Track post-release issues and link them back to originating PRs.

• Introduce AI-powered static analysis or pre-merge validation.

• Conduct brief post-incident retrospectives to refine review focus.

7. Reviewer Load Balance

What it measures:

How evenly are code review responsibilities distributed across team members?

Why it matters:

If the same reviewers handle most PRs, they become bottlenecks, reviews slow down, and burnout risk increases. Balanced workloads improve both speed and team morale.

How to improve it:

• Rotate reviewers based on expertise and availability.

• Use tools like Entelligence AI PR Dashboard to route reviews automatically.

• Track review participation rates weekly.

8. Review Participation Rate

What it measures:

The percentage of developers actively involved in code reviews each sprint or release cycle.

Why it matters:

A healthy participation rate shows collective ownership and cross-team knowledge sharing. Low participation often means review responsibilities are unclear or unbalanced.

How to improve it:

• Encourage all developers, not just senior ones, to review code regularly.

• Include review contribution as part of performance discussions.

9. Defect Density in Reviewed Code

What it measures:

The number of defects found per line of code (LOC) during reviews.

Why it matters:

A decreasing defect density over time shows that the team is learning from past reviews and writing cleaner code.

How to improve it:

• Track recurring issues and create team guidelines to address them.

• Use AI insights to identify patterns in frequent review comments.

10. Review Sentiment and Constructiveness (Qualitative Metric)

What it measures:

The kind of feedback you get and how helpful, specific, and polite your comments are.

Why it matters:

Review culture directly affects collaboration and developer morale. Negative or vague comments discourage participation; constructive feedback strengthens team trust.

How to improve it:

• Set communication guidelines for reviews.

• Encourage phrasing like “Could we try…” instead of “This is wrong.”

• Periodically review PR comment samples to ensure clarity and tone.

Performance evaluation is only one part of the picture. The next step is to give teams the tools they need to act on those insights. This is where Entelligence AI comes in.

How Entelligence AI Helps You Do Better Code Reviews?

Even the most well-defined review process can slow teams down when feedback loops are long, reviews pile up, or inconsistencies creep in. Reviewers waste time on repetitive checks, PRs stay open for days, and developers struggle to maintain context across changes. Without visibility or automation, code reviews often become a bottleneck. 

That’s where Entelligence.ai comes in. It transforms traditional reviews into a smarter, AI-assisted workflow that’s faster, fairer, and more insightful. By integrating directly with your IDE, GitHub, and CI/CD pipeline, it eliminates redundant effort and gives every reviewer the context they need from the start.

Here’s how Entelligence.ai makes it happen:

1. AI-Powered PR Summaries

Generates clear, simple summaries of each pull request automatically, highlighting what changed, why it matters, and which parts are affected. Reviewers start with all the facts, not just a guess.

2. Smart Review Prioritization

Identifies high-risk changes, such as logic-heavy or security-sensitive files, so reviewers can focus where it counts instead of scanning low-impact edits.

3. Automated Quality Checks

Runs pre-review scans for missing tests, style issues, and potential bugs. Reviewers skip repetitive nitpicks and focus on architecture and intent.

4. Context-Aware Suggestions

Intelligent inline feedback in the IDE helps authors fix problems before sending PRs, reducing back-and-forth and review work.

5. Team-Level Insights

Tracks key metrics like review turnaround time, merge speed, and reviewer workload. Managers get visibility into bottlenecks and process health without manual tracking.

6. Continuous Learning

Learns from your team’s standards, feedback, and historical reviews to deliver more accurate and consistent recommendations over time.

You and your team can review faster, work together better, and maintain higher quality with Entelligence AI, all without adding more steps to the process.

Conclusion

Great engineering goes beyond writing code. It is about reviewing as well. A strong code review process builds trust, improves quality, and keeps teams aligned as products scale. But without structure, consistency, and visibility, reviews can slow progress rather than drive it.

With Entelligence AI, the process becomes effortless. From AI-powered summaries to automated checks and real-time team insights, every review becomes sharper, faster, and more impactful.

Clarity drives progress, and with Entelligence, every review brings you closer to it. Ready to see how smarter reviews improve your entire development workflow? Start your free trial of Entelligence AI today.

Frequently Asked Questions

Q1. How do you do a good code review?

A good code review focuses on readability, logic, and maintainability rather than minor formatting issues. Keep reviews small, provide clear and respectful feedback, and always ensure the reviewer has enough context about the change. Using automated linting and testing tools can save time and improve accuracy.

Q2. How long should a code review take?

Most effective reviews happen within 24–48 hours of submission. Shorter feedback loops maintain momentum and reduce context switching. Research suggests that reviewing fewer than 400 lines of code at a time leads to higher accuracy and faster turnaround.

Q3. What are the biggest challenges in code reviews?

Common challenges include slow review cycles, inconsistent standards, lack of context, and overly nitpicky feedback. Teams also struggle with large pull requests and unclear communication. Defining clear guidelines and leveraging AI-powered review tools can help overcome these issues.

Q4. What metrics should teams track for effective code reviews?

Key metrics include review turnaround time, defects found per review, reviewer participation, and code churn rate. Tracking these helps teams identify bottlenecks and continuously refine their review process.