What Is Peer Code Review? | phoenixNAP IT Glossary

Peer code review is a common software development practice in which developers examine each other’s code before it is merged or released.

What Is a Peer Code Review?

Peer code review is a quality control step in the software development lifecycle where one or more developers evaluate a change to the codebase, usually a commit, patch, or pull/merge request, before it is integrated into the main branch or deployed.

The review focuses on whether the change is correct, safe, and maintainable: reviewers check that the code implements the intended behavior, handles edge cases, avoids regressions, and aligns with the project’s architecture, style conventions, and engineering standards. It also serves as a risk-reduction mechanism by creating a second set of eyes on changes that could introduce security issues, performance problems, unreliable error handling, or unintended side effects across dependent modules.

Types of Peer Code Review

Peer code review can take several forms depending on the team’s workflow, tooling, and how quickly feedback is needed. These are the most common types you’ll see in practice.

Asynchronous Tool-Based Review (Pull/Merge Request Review)

This is the most common approach in modern teams using Git-based platforms. A developer opens a pull request (or merge request) and reviewers comment on the diff when they have time. It creates a durable record of feedback, supports inline discussion, and works well for distributed teams, but it can slow down delivery if reviewers are unavailable or if the change is large and hard to understand.

Synchronous Over-the-Shoulder Review

In an over-the-shoulder review, the author walks a reviewer through the change in real time, often at a desk, in a quick call, or via screen share. It’s fast for small, time-sensitive changes and helps clarify intent immediately, but it doesn’t always produce a strong written trail of decisions unless the key outcomes are summarized in the code review tool afterward.

Pair Programming as Continuous Review

With pair programming, two developers work on the same change together, switching roles between “driver” and “navigator.” This effectively embeds review into development, catching issues early and improving design quality as the code is written. It can reduce the need for heavy post-hoc review, but it requires scheduling coordination and may be less efficient for straightforward tasks.

Formal Inspection (Structured Code Inspection)

A formal inspection is a highly structured review with defined roles (author, moderator, reviewers) and explicit entry/exit criteria. Teams use it for high-risk code such as security-critical components, safety-related systems, or regulated environments. It’s thorough and measurable, but it is time-intensive and usually reserved for code where the cost of defects is especially high.

Email or Patch-Based Review

In patch-based workflows, the author sends a patch (or series of patches) to reviewers, often via email or a specialized review system, and feedback is provided in threaded replies. This model is common in some open-source communities and low-bandwidth environments. It’s lightweight and works without a centralized platform, but discussions can be harder to track and consolidate compared to modern PR tools.

Team Review/Group Walkthrough

A team review involves presenting the change to a small group (sometimes during a scheduled session) so multiple perspectives can spot issues in logic, design, testing, or operational impact. It’s useful for cross-cutting changes that affect multiple services or teams, but it’s more expensive in people-time and can be overkill for routine updates.

How Does Peer Code Review Work?

Peer code review is the process of having another developer validate a code change before it becomes part of the shared codebase. The goal is to catch issues early, confirm the change matches its intent, and make the code easier to maintain. Here is exactly how the process works:

Prepare a focused change. The author implements the update in a feature branch and keeps the diff as small and cohesive as possible, so reviewers can understand the intent quickly and spot problems without wading through unrelated edits.
Open a review request with context. The author creates a pull/merge request and explains what the change does, why it’s needed, and how to validate it. This gives reviewers a clear target and reduces back-and-forth about assumptions.
Run automated checks first. CI pipelines execute builds, linters, security checks, and tests to catch obvious failures early. This ensures reviewers spend their time on higher-value concerns like logic, design, and edge cases.
Reviewers examine the diff and behavior. Reviewers read the code with the change’s intent in mind, looking for correctness, clarity, consistency with conventions, and potential side effects. This step is where subtle bugs, missing validations, and maintainability issues are most often found.
Leave actionable feedback and discuss tradeoffs. Reviewers add comments or suggestions, marking what must be fixed versus what’s optional. The discussion helps align on design choices, reduces ambiguity, and spreads knowledge across the team.
Revise and re-verify. The author addresses the feedback, updates the code and tests, and re-runs checks. This tight loop turns review input into concrete improvements and confirms that fixes didn’t introduce new issues.
Approve and merge with traceability. Once reviewers are satisfied and checks pass, the change is approved and merged, leaving a recorded history of decisions. This protects the main branch, supports future troubleshooting, and sets a consistent quality bar for the codebase.

Peer Code Review Best Practices

Good peer code reviews are consistent, lightweight, and focused on improving the code without slowing delivery. These best practices help teams keep reviews high-quality and low-friction:

Keep changes small and single-purpose. Smaller pull requests are easier to understand, review faster, and reduce the risk of missing issues buried in noise.
Provide clear context in the description. State the goal, approach, and any tradeoffs, plus how to test or verify the change, so reviewers don’t have to infer intent from the diff alone.
Run automated checks before requesting review. Make sure formatting, linting, builds, and tests pass so human review time goes to logic, design, and risk, and not avoidable failures.
Review for correctness first, then maintainability. Prioritize bugs, edge cases, error handling, and security implications before discussing style or refactors.
Use a consistent checklist. Scan for inputs/validation, failure paths, concurrency/state issues, performance hotspots, logging/metrics, and test coverage to avoid blind spots.
Ask for tests that match the risk. Ensure critical paths and bug fixes have coverage (unit/integration as appropriate) and that tests are meaningful and not just added for quota.
Make feedback specific and actionable. Point to exact lines, explain the concern, and propose an alternative when possible to reduce back-and-forth.
Separate “must fix” from “nice to have.” Label blockers versus suggestions so the author knows what’s required to merge and what can be deferred.
Avoid bikeshedding; align on standards. Use shared style rules and linters/formatters to settle formatting debates automatically and keep the discussion on substance.
Be respectful and assume positive intent. Phrase comments about the code, not the person, to keep the process collaborative and psychologically safe.
Set review SLAs and rotate reviewers. Agree on expected response times and share review load to prevent bottlenecks and reviewer burnout.
Summarize decisions for non-trivial discussions. Capture key outcomes in the PR thread or description so future readers understand why choices were made.

Peer Code Review Tools

Peer code review tools help teams share code changes, discuss them in context, and enforce quality gates (tests, approvals, policies) before merging. Here are widely used options and what they’re best at:

GitHub pull requests. Provides inline diff comments, threaded discussions, requested reviewers, required checks, and branch protection rules. Strong ecosystem for CI integrations (Actions) and code ownership rules, making it a common default for teams hosting code on GitHub.
GitLab merge requests. Combines review with CI/CD pipelines, environments, and deployment workflows in one place. Supports approvals, code owners, review apps, and rich MR templates, which works well for teams that want code review tightly coupled with delivery.
Bitbucket pull requests. Integrates cleanly with Atlassian tooling (Jira, Confluence, Bamboo). Useful for organizations already standardized on Atlassian, with features for approvals, tasks, and merge checks to enforce process.
Azure DevOps repos (pull requests). Built for enterprise workflows with fine-grained permissions, policies, and integration with Azure Pipelines and work items. Often chosen in Microsoft-heavy environments where traceability and governance are key.
Gerrit code review. A dedicated code review system centered on reviewing individual commits (“changes”) before they land, with powerful access controls and a mature review workflow. Common in large, high-discipline engineering organizations and some open-source communities.
Phabricator (differential). Provides code review plus task tracking and a suite of developer tools. While many teams have migrated away, it’s still used in some environments because of its integrated workflow and review features.
Crucible. An Atlassian review tool historically used alongside Bitbucket Server and Jira for formal review processes. It’s more common in legacy setups where teams want structured, audit-friendly reviews.
Review board. A standalone review platform that supports multiple version control systems and patch-based reviews. Useful when you need a centralized review workflow without moving repositories to a specific hosting provider.
Email/patch-based workflows (e.g., mailing lists with diff tools). Common in certain open-source projects and kernel-style development. Reviews happen as discussions on patches sent via email, which can be lightweight and decentralized but requires discipline to track feedback and versions.
Code collaboration add-ons (optional but common) - Code owners + static analysis. Not full review tools on their own, but often paired with PR systems. CODEOWNERS/approval rules route reviews to the right people, while static analysis tools (linters, SAST, dependency scanners) add automated feedback directly into the review.

The Benefits and the Challenges of Peer Code Reviews

Peer code reviews can significantly improve software quality and team consistency, but they also introduce overhead and depend on good habits to work well. The following benefits and challenges highlight what teams typically gain from code review, and what can slow it down or make it less effective.

What Are the Benefits of Peer Code Reviews?

Peer code reviews improve the quality and reliability of code by adding a second set of eyes before changes are merged. They also strengthen how teams collaborate and maintain shared standards over time. They include:

Fewer defects reach production. Reviewers often catch logic errors, missed edge cases, and unintended side effects that automated tests or the author might miss.
Better maintainability and readability. Feedback on naming, structure, and complexity helps keep code easier to understand, refactor, and troubleshoot later.
More consistent standards across the codebase. Reviews reinforce conventions for style, architecture, and patterns, reducing fragmentation across modules and teams.
Improved security and risk awareness. Reviewers can spot risky input handling, authorization gaps, unsafe dependencies, and insecure patterns before they ship.
Stronger test coverage and safer changes. Reviews push for meaningful unit/integration tests and ensure changes are verifiable, which reduces regression risk.
Knowledge sharing and reduced silos. As reviewers learn new areas of the code and authors explain decisions, the team spreads context and avoids single points of failure.
Higher-quality design decisions. Reviews create a checkpoint to challenge assumptions, validate approaches, and catch architectural drift early.
Better onboarding and continuous learning. Newer developers learn patterns and expectations by reading reviews and receiving targeted feedback on real code.
Traceability and accountability. Review threads document what changed and why, which helps with audits, incident analysis, and future maintenance.

What Are the Challenges of Peer Code Reviews?

Peer code reviews bring clear quality gains, but they can also slow delivery or become inconsistent if the process isn’t managed well. These are the most common challenges teams run into:

Slower throughput and longer cycle time. Reviews add a waiting step, and work can stall if reviewers aren’t available or if approvals are required from busy specialists.
Large or unfocused pull requests. Big diffs are hard to understand, increase cognitive load, and make it easier to miss bugs or important design issues.
Inconsistent review quality. Different reviewers may focus on different things, leading to uneven standards, missed risks, or contradictory feedback across the team.
Bikeshedding and style debates. Time can be wasted on minor preferences (formatting, naming nitpicks) instead of correctness and maintainability, especially without shared rules or automated formatting.
Unclear expectations for “done.” If it’s not explicit what is required to merge (tests, approvals, and performance checks) authors can get stuck in repeated rounds of revisions.
Context gaps and hidden dependencies. Reviewers may not know the domain, legacy constraints, or downstream impact, which can lead to shallow reviews or incorrect assumptions.
Social friction and psychological safety issues. Poorly phrased feedback, power dynamics, or public criticism can make reviews defensive, reducing candor and collaboration.
Over-reliance on review to catch everything. Teams may treat review as a safety net and underinvest in tests, monitoring, and automation, even though review can’t reliably detect all issues.
Security and compliance bottlenecks. Requiring specialized reviewers (security, privacy, platform) can create queues, especially if the request volume is high or the rules are rigid.

Peer Code Review FAQ

Here are the answers to the most commonly asked questions about peer code reviews.

How Long Does Peer Code Review Usually Take?

Peer code review can take anywhere from a few minutes to a couple of days, but for a typical, reasonably sized pull request many teams aim to get the first review response within a few hours and complete the review within 24–48 hours.

Small, focused changes with clear context and passing CI often get approved quickly, while larger or higher-risk changes take longer because reviewers need more time to understand impact, ask questions, and verify tests, especially if multiple reviewers or specialist approvals are required.

What Not to Do in a Peer Code Review?

In a peer code review, avoid behaviors that reduce quality, slow delivery, or create friction. Don’t review huge, unfocused changes without asking the author to split them, as this makes meaningful feedback unlikely. Don’t focus on personal style preferences or minor formatting issues when automated tools can handle them, and don’t bikeshed at the expense of correctness and risk.

Avoid vague comments like “this looks wrong” without explaining why or suggesting a fix, and don’t mix required changes with optional suggestions without clearly labeling them. Don’t rush approvals without understanding the intent or impact of the change, but also don’t block progress by nitpicking or repeatedly reopening settled decisions.

Finally, don’t make reviews personal. Rather, criticize the code, not the developer, and keep feedback respectful and constructive.

What Is the Future of Peer Code Review?

The future of peer code review is moving toward a more automated, faster, and risk-focused process that complements human judgment rather than replacing it. AI-assisted reviews are increasingly used to flag common bugs, security issues, performance risks, and style problems before a human even looks at the code, allowing reviewers to focus on intent, design, and edge cases. Teams are also shifting toward smaller, continuous reviews integrated into development through pair programming, trunk-based workflows, and stronger CI gates.

As systems grow more complex, peer code review is likely to become less about line-by-line scrutiny and more about validating correctness, safety, and architectural alignment, with automation handling routine checks and humans concentrating on decisions that require context and experience.