Shipping software quickly matters. However, shipping code that the team can understand, maintain, test, and safely change six months from now matters even more. Consequently, that is where code review earns its place in a healthy engineering process.
As a Tech Lead or Engineering Manager, I don’t see code review as a final checkpoint where a senior developer decides whether someone’s work is “good enough.” Instead, that mindset creates the wrong culture. A good review is ultimately a conversation about making the codebase better while helping engineers share knowledge and catch problems before customers find them.
Done well, code review improves more than code quality. For instance, it spreads knowledge across the team, exposes design problems early, encourages better testing, and reduces the chance that one engineer becomes the only person who understands an important part of the system. Conversely, done badly, it becomes a major bottleneck.
As a result, developers wait hours or days for approvals, reviewers argue over naming, pull requests grow to thousands of lines, and comments become personal. Eventually, people start treating the process as something they need to get through rather than something that helps them build better software. Ultimately, the difference comes down to the process and culture surrounding the review.
Therefore, here are 12 practical code review practices I have found most valuable for modern software teams.
What Is Code Review?
Code review is the process of having another developer examine a proposed code change before that change becomes part of the main codebase. In practice, this happens through a pull request or merge request within most modern development teams. Typically, a developer creates a branch, makes changes, tests those changes, and opens a request to merge them. Thereafter, one or more engineers examine the proposed change.
At this stage, the reviewer isn’t simply asking: “Does this code compile?”
Rather, the real questions are broader:
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Does the change solve the intended problem?
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Is the design appropriate?
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Could it introduce unexpected behavior?
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Is the code easy to understand?
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Are important edge cases handled?
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Are the tests meaningful?
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Could the change create security or performance problems?
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Furthermore, will another engineer understand this code later?
In fact, Google’s Engineering Practices documentation describes code review as part of maintaining code and product quality. Specifically, its reviewer guidance includes areas such as design, functionality, complexity, testing, naming, comments, style, and documentation. Undoubtedly, that is a much better definition of review than simply looking for syntax errors.
Why Code Review Matters
Automated tools are extremely useful. For example, a modern CI pipeline can run unit tests, integration tests, static analysis, formatting checks, dependency scans, security tools, and many other automated checks. However, automation doesn’t understand every engineering decision.
On one hand, a test suite may tell you that a function returns the expected result, yet it may not tell you that the developer created unnecessary complexity that will make the feature painful to maintain. Similarly, a linter can identify formatting problems, but it probably won’t tell you that the team is creating a second implementation of logic that already exists somewhere else.
Therefore, code review adds human judgment. Additionally, it can spread knowledge. Specifically, when engineers regularly review each other’s work, knowledge about the system moves through the team instead of remaining with individual developers. As a result, that reduces an important engineering risk: knowledge silos.
A Practical Code Review Process
The most effective process actually starts before a reviewer opens the pull request.
Step 1: Understand the Problem
The author should be able to explain what the change is supposed to accomplish. For instance, a useful pull request description might explain:
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What problem is being solved
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Why the change is necessary
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The general approach
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Important technical decisions
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Testing performed
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Known limitations
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Related tickets or documentation
In short, a reviewer shouldn’t have to reverse-engineer the business requirement from the code.
Step 2: Keep the Change Focused
Generally, a pull request should solve one clear problem. Imagine reviewing a pull request that contains:
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A bug fix
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A database migration
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A major refactor
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Formatting changes
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Dependency upgrades
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New API endpoints
Even if every change is correct, the combination makes the review harder. In contrast, smaller changes allow reviewers to understand intent more quickly and reason about risk more accurately. Indeed, Google’s engineering guidance specifically recommends small changes because they tend to be reviewed faster, more thoroughly, and are easier to reason about.
Step 3: Let Automation Run First
Human reviewers should not spend most of their time finding problems a machine can identify automatically. Therefore, before requesting review, run the appropriate automated checks.
Depending on the project, these might include:
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Build checks
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Unit tests
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Integration tests
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Linters
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Formatters
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Static analysis
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Security scanning
Ultimately, automation should handle repeatable rules, whereas humans should spend their attention on engineering judgment.
Step 4: Choose the Right Reviewer
Of course, not every developer is the best reviewer for every change. For example:
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A database migration may need someone familiar with the data model.
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Authentication code may need someone with security experience.
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A frontend architecture change may need someone familiar with the UI architecture.
Ultimately, the goal isn’t to find the highest-ranking engineer available; rather, the goal is to find someone who understands the area well enough to evaluate the change properly.
Step 5: Review the Change in Context
Before reading individual lines, first understand the purpose of the change by asking:
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What is this supposed to accomplish?
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What behavior existed before?
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What behavior will exist afterward?
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Furthermore, what could go wrong?
Afterward, inspect the implementation. Consequently, this avoids a common problem where reviewers spend ten minutes discussing variable names while missing a flaw in the overall approach.
Step 6: Resolve Important Feedback
Not every comment should block a merge. Instead, teams should distinguish between them because:
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Some feedback identifies a real correctness issue.
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Some suggests an improvement.
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Some is simply personal preference.
To achieve this, labels such as these can help:
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Blocking: This must be resolved before merging.
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Suggestion: I think this would improve the implementation.
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Question: I need clarification about this decision.
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Nit: Minor improvement that should not prevent approval.
As a result, that small amount of clarity can prevent surprisingly long discussions.
12 Code Review Practices That Work
1. Review the Design Before the Details
First and foremost, start with architecture and behavior. Ask whether the solution fits the existing system and whether a simpler approach exists. After all, there is little value in polishing individual lines if the overall design is wrong.
2. Keep Pull Requests Small
Large reviews inevitably create cognitive overload. For instance, SmartBear’s research-based guidance recommends reviewing fewer than roughly 400 lines at a time and notes that effectiveness drops as reviews become too large.
However, that doesn’t mean 401 lines automatically makes a bad pull request. Instead, treat the number as guidance rather than a rigid law. The overarching principle matters more: namely, smaller changes are easier to understand.
3. Review for Correctness First
Above all, the most important question is whether the code works correctly. Therefore, look for:
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Incorrect assumptions
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Boundary conditions
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Missing error handling
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Race conditions
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Null or empty values
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Failure scenarios
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Unexpected input
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Data consistency problems
In short, a formatting issue is annoying, whereas a payment calculation bug is expensive. Priorities matter.
4. Look for Unnecessary Complexity
Complex code always creates future maintenance costs. Thus, during review, ask whether the implementation could be simpler.
To be fair, sometimes complexity is necessary. Distributed systems, concurrency, security, and performance-sensitive software can require sophisticated solutions. However, complexity should solve a real problem—it should not exist simply because the solution looks clever.
5. Check the Tests
Don’t only check whether tests exist; instead, read them carefully. Ask whether they test the important behavior and whether they would fail if the implementation were wrong.
In fact, good tests often reveal the author’s understanding of the requirements. For example, for a bug fix, I especially like seeing a test that reproduces the original problem and proves that it won’t quietly return later.
6. Review Security Implications
Security should not be reserved solely for a separate security audit. Instead, reviewers should watch for obvious risks such as:
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Missing authorization
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Weak input validation
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Exposed secrets
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Unsafe queries
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Sensitive data in logs
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Insecure dependencies
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Incorrect permission checks
However, for high-risk changes, involve someone with security expertise rather than expecting every developer to be a security specialist.
7. Check Performance Where It Matters
Not every pull request needs a deep performance investigation. Nevertheless, changes involving databases, loops over large datasets, network calls, caching, or high-traffic services deserve extra attention.
For example, a query that performs well with 100 rows may behave very differently with 10 million. Hence, review the likely production conditions, not only the developer’s laptop.
8. Automate Style Rules
One of the biggest wastes of review time is arguing about formatting that a tool could enforce. Therefore, if the team cares about indentation, import ordering, line length, naming rules, or formatting conventions, automate as much of it as possible. Consequently, that leaves reviewers with more mental energy for design and correctness.
9. Explain Why
Compare these two comments:
“Change this.”
versus:
“This function is called from several request handlers. Could we move this validation into the shared service so the behavior stays consistent?”
Clearly, the second comment explains the reasoning behind the request. That matters because a useful code review teaches as well as corrects.
10. Review the Code, Not the Developer
Never turn technical feedback into personal criticism. For example:
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Avoid: “You wrote this badly.”
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Prefer: “This implementation may become difficult to maintain because the validation logic appears in three places.”
Ultimately, the subject of the discussion is the code itself. Therefore, that distinction is essential for building a healthy engineering culture.
11. Respond Quickly
A technically perfect review that arrives three days late can still damage engineering velocity. Indeed, waiting for reviews creates idle time, context switching, merge conflicts, and frustration.
Thus, teams should treat review work as part of normal engineering work rather than something developers handle only after finishing everything else. Review speed matters because feedback is most useful while the change is still fresh in the author’s mind.
12. Use Reviews to Share Knowledge
Finally, code review is one of the easiest ways to spread system knowledge across a team. For instance:
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A backend developer reviewing authentication changes learns more about security architecture.
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A junior engineer reviewing a senior engineer’s pull request learns design patterns used in the real codebase.
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A senior engineer reviewing a newer developer’s work gets an opportunity to explain why certain architectural decisions exist.
Over time, reviews create shared ownership, which is undoubtedly one of their biggest benefits.
Common Code Review Mistakes
Even good teams can still develop bad review habits. Here are several common traps I watch for:
Rubber-Stamp Reviews
A pull request appears. Someone opens it, and thirty seconds later types: “LGTM” and approves it. However, that isn’t a meaningful review. Approval should mean the reviewer has enough confidence in the change to allow it into the shared codebase.
Reviewing Only Style
Formatting and naming matter, but they shouldn’t consume the entire review. If reviewers spend their attention on semicolons while missing broken authorization logic, the process has failed. Instead, automate style wherever practical.
Giant Pull Requests
A 3,000-line change is technically reviewable; realistically, however, reviewers may just skim it. Large changes increase fatigue and make relationships between changes harder to understand. Therefore, break work into logical pieces whenever possible.
Treating Opinions as Requirements
Developers naturally have preferences. One engineer likes one pattern, while another prefers something different. Before blocking a pull request, ask: “Is this actually a problem, or would I simply have written it differently?” In practice, that question prevents many unnecessary arguments.
Slow Reviews
Long delays encourage larger batches of work and more context switching. Additionally, they make merge conflicts much more likely. Hence, teams should establish reasonable expectations for review response times.
Using Review as a Power Tool
Code review should not become a hierarchy exercise where senior engineers prove that they know more than junior engineers. In truth, a reviewer has responsibility, not authority for its own sake. The goal is simply better software.
Depending on Code Review to Catch Everything
Review is only one layer of quality control. Therefore, it does not replace:
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Testing
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Monitoring
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Observability
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Security controls
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CI/CD checks
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Architecture practices
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Production safeguards
In short, strong engineering systems use multiple layers.
Code Review and AI-Generated Code
AI coding tools have made review even more important. Today, developers can generate functions, tests, APIs, database queries, and sometimes entire features much faster than before. However, generated code still needs engineering judgment.
In fact, a piece of code can look clean and convincing while containing incorrect assumptions about the surrounding system. Therefore, when reviewing AI-assisted code, I pay particular attention to:
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Invented APIs
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Incorrect library usage
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Missing edge cases
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Weak security assumptions
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Unnecessary dependencies
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Duplicate functionality
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Tests that only confirm the generated implementation
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Code that works locally but conflicts with system architecture
Granted, AI can also assist reviewers by identifying suspicious patterns or summarizing large changes. That’s useful. Nevertheless, I would treat AI review as another automated layer, not as the final engineering decision. Ultimately, the person approving a change should still understand what is entering the codebase.
Creating a Healthy Code Review Culture
Process alone isn’t enough; in addition, teams need psychological safety around technical disagreement. Developers should be able to ask: “Why did we choose this approach?” without that question being interpreted as criticism. Likewise, authors should be comfortable responding: “I considered that approach, but I chose this one because…”
Ultimately, strong teams debate ideas without turning those debates into personal battles.
Tech Leads and Engineering Managers have an important role here. Specifically, we set the standard through our own reviews:
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If senior engineers write aggressive comments, junior engineers will copy them.
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If leaders spend every review arguing about personal preferences, the team will learn that code review is about pleasing reviewers.
Instead, model curiosity. Ask questions, explain reasoning, acknowledge good decisions, and separate required changes from optional suggestions. As a result, that creates an environment where engineers actually want thoughtful feedback.
A Simple Code Review Checklist
Before approving a change, I generally want confidence in five areas:
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Purpose: Does the change solve the intended problem?
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Correctness: Does the implementation behave properly, including important edge cases?
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Maintainability: Can another engineer understand and safely modify it later?
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Testing: Do the tests provide meaningful protection against regressions?
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Risk: Could the change introduce security, performance, reliability, or operational problems?
Crucially, the checklist does not need to become bureaucracy. Rather, its job is simply to remind reviewers where their attention creates the most value.
Final Thoughts
Good code review isn’t about proving who is the strongest developer in the room. Instead, it is about protecting the codebase while helping the team move forward. Indeed, the best reviews catch defects, challenge unnecessary complexity, improve designs, spread knowledge, and help engineers learn from each other.
However, the process needs balance. Make pull requests small enough to understand, automate repetitive checks, choose reviewers who understand the area, focus on correctness before style, explain why a change matters, and respond quickly. Most importantly, keep technical feedback focused on the code rather than the person who wrote it.
When those habits become normal, code review stops feeling like a gate developers must pass. Instead, it becomes part of how the engineering team thinks together. And ultimately, that is what a mature review process should accomplish.
Frequently Asked Questions About Code Review
What is code review in software engineering?
Code review is the process of having another developer examine proposed source code changes before they are merged into the main codebase. Specifically, the reviewer checks areas such as correctness, design, maintainability, testing, security, and possible side effects.
Why is code review important?
Code review can identify bugs before production, improve maintainability, encourage consistent engineering practices, spread knowledge across the team, and furthermore provide opportunities for developers to learn from each other.
Who should perform a code review?
Ideally, the reviewer should understand the part of the system being changed. However, complex changes may require multiple reviewers with different areas of expertise, such as database, frontend, infrastructure, or security knowledge.
How large should a code review be?
Smaller reviews are generally easier to understand and review thoroughly. For instance, SmartBear recommends keeping individual review sessions below roughly 400 lines of code where practical. Similarly, Google encourages small, self-contained changes because they are easier and faster to review.
How long should a code review take?
There is no universal time limit because complexity varies. For example, a small change may require only a few minutes, while an architectural change may require much longer. What matters ultimately is avoiding reviews that are so large or long that reviewer attention drops.
What should reviewers look for during code review?
Reviewers should examine functionality, design, complexity, tests, readability, naming, documentation, security, performance, error handling, and overall whether the change fits the existing architecture.
Should code reviewers check formatting?
Only when formatting cannot be automated. Otherwise, linters and formatters should handle most mechanical style rules so reviewers can focus on problems requiring human judgment.
Can code review replace testing?
No. Code review and testing solve different problems. Specifically, automated tests verify expected behavior, whereas human review can identify design problems, maintainability issues, incorrect assumptions, missing scenarios, and architectural concerns.
Should every pull request require approval?
That depends on the team’s risk level and development workflow. For example, many organizations require at least one review before changes reach protected branches, while higher-risk areas may require additional reviewers or specific domain experts.
Can AI perform code review?
AI tools can help identify potential bugs, security issues, code smells, or unusual patterns. Additionally, they can summarize large changes. However, AI review should normally complement human engineering judgment rather than completely replace it.
What makes a bad code review?
Common problems include rubber-stamp approvals, huge pull requests, slow responses, excessive focus on formatting, vague comments, personal criticism, and finally, reviewers treating personal preferences as mandatory engineering standards.
What makes a good code review comment?
A good comment is specific, respectful, and actionable. In short, it explains what the reviewer noticed, why it matters, and—when appropriate—suggests a possible improvement. Furthermore, it should make clear whether the issue is blocking or simply a suggestion.
References
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Google Engineering Practices — Code Review Developer Guide
Google describes code review as a process used to maintain code and product quality and provides detailed guidance for reviewers and authors.
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Google Engineering Practices — The Standard of Code Review
Google’s review standard focuses on improving the overall health of a codebase over time while balancing quality with developer progress.
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Google Engineering Practices — Small CLs
Google explains why smaller changes tend to receive faster and more thorough reviews and are easier to reason about and merge.
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GitHub Docs — Reviewing Proposed Changes in a Pull Request
GitHub documents the practical workflow for examining changed files, leaving feedback, approving changes, and requesting updates.
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GitLab — Code Review Guidelines
GitLab’s engineering documentation explains its review workflow and emphasizes effective, understandable, maintainable, and secure code.
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SmartBear — Best Practices for Code Review
SmartBear provides research-backed recommendations concerning review size, review speed, defect detection, metrics, and healthy peer-review culture.