How to Review Code | Tips and Best Practices

Great teams use code reviews as a quality gate, not a checkbox. Done right, they catch defects early, improve maintainability, and lift developer productivity. Done poorly, they become nitpicks, slow merges, or rubber-stamp approvals.

Why code reviews matter (fast recap):

• Catch bugs before production (fixes are far cheaper pre-merge).

• Improve design, readability, and long-term maintainability.

• Spread knowledge across the team and onboard faster.

What this guide covers:

• Code review best practices for small, focused PRs and clear context.

• How to review code for design, correctness, tests, and risk, efficiently.

• Writing comments that teach (not torch), and resolving pushback gracefully.

• Using AI code review tools to automate the trivial so humans focus on architecture and intent.

• A lightweight code review process with metrics to keep throughput high.

Let’s dive in.

Establish Clear Code Review Guidelines and Standards

Before you review code, align on the code review process and outcomes. Clarity turns ad-hoc code reviews into a repeatable system that boosts developer productivity and code health.

Define “good” upfront (what makes a good code review)

Document good code review practices so every code reviewer knows the bar for good code and better reviews:

• Readability & clarity: names, comments, and intent are obvious.

• Architecture & design: follows principles; avoids tight coupling; fits the system.

• Correctness & risk: handles edge cases; errors are surfaced; no hidden regressions.

• Security & compliance: no secrets; OWASP-class issues addressed; policies met.

• Tests: meaningful unit/integration tests; failure modes covered.

• Consistency: complies with style guide; aligns with existing patterns.

These are foundational code review best practices, use them as acceptance criteria when you perform a code review.

Put it in a checklist (make it operational)

Create a lightweight checklist in your PR template so code reviewers run the same play every time:

• Problem statement & scope present

• Impacted modules listed

• Tests added/updated

• Security considerations noted

• Rollback/monitoring plan (if relevant)

A checklist standardizes software code review, shortens cycles, and raises review depth, key developer productivity metrics for productivity in engineering.

Separate humans from bots (and reduce noise)

State clearly what is out of scope for manual coding review:

• Linters/formatters auto-fix formatting, imports, spacing.

• Static analysis / SAST flags obvious bugs and smells.

• AI code review tools (e.g., CodeAnt.ai) surface complexity, duplication, test gaps, and security findings directly in the PR.

Humans focus on architecture, trade-offs, and maintainability; automation handles the rest. This is how high-performing teams do code reviews without killing software developer productivity.

Classify feedback: blocking vs. non-blocking

Define severity so decisions are consistent across code reviewers:

• Blocking: correctness, security, major maintainability, policy violations.

• Non-blocking / suggestions: style, small refactors, naming tweaks.

• Use “Nit:” to mark optional comments (that’s literally what “nit means in code review”). It keeps threads focused and reduces friction.

Measure and iterate (close the loop)

Track developer metrics tied to the code review process:

• Time to first review and time to merge

• PR size bands vs. defect rate

• Review iterations per PR and reviewer load

Use these signals to refine guidelines (e.g., cap PR size, adjust policy gates). With AI assistance, you’ll get better reviews faster, and a clear picture of how to code review effectively at scale.

TL;DR: 

Write down what are code reviews for your team, encode the rules in a checklist, automate the trivial with code review tools, and classify feedback. That’s how to do code review consistently, improve developer productivity, and keep quality rising with every PR.

Prepare Your Code Before the Review (Author’s Checklist)

Great code reviews start with a review-ready PR. As the author, your prep determines whether teammates can review code quickly and focus on substance. Use this checklist to streamline the code review process, improve developer productivity, and set up better reviews.

1. Prove it works

• Run the full test suite (unit + integration) and build locally/CI.

• Fix flakiness and failures before assigning reviewers.

• If performance is relevant, include a quick note or numbers.

Why it helps: avoids “it doesn’t run” threads and shortens time-to-merge, one of the key developer productivity metrics.

2. Self-review the diff

• Remove debug prints, dead code, and commented-out blocks.

• Apply formatters/linters so humans don’t chase spacing or imports.

• Skim every hunk and leave yourself comments/todos you then resolve.

Tip: let AI code review tools (e.g., CodeAnt AI) pre-scan for style, code duplication, secrets, and obvious bugs so humans focus on logic and design.

3. Write a crisp PR description

• One-line summary + “why now.”

• Scope, risks, and the approach (with links to tickets/design docs).

• Call out trade-offs, assumptions, and any follow-ups.

• If UI or API changes: add screenshots, sample requests/responses.

Example: “Fix payment timeout: raise API timeout from 5s→10s, add retry with jitter; guards added for idempotency.”

4. Keep the PR small and focused

• Prefer slices (feature flags, isolated refactors) over mega-PRs.

• Organize commits logically; avoid mixing reformatting with logic.

• If a large change is unavoidable, state how to perform a code review on it (review order, key files first).

Small, focused PRs are core code review best practices and correlate with faster, higher-quality software code review.

5. Include tests and edge cases

• Add/extend unit and integration tests that prove behavior and failure modes.

• Cover empty inputs, error paths, concurrency, and boundary conditions.

• Note any intentional gaps (and create follow-up issues).

AI assist: CodeAnt AI flags missing tests on critical paths and suggests cases, use it to raise coverage with less toil.

6. Security, compliance, and policy checks

• Validate inputs, sanitize outputs, protect secrets, and avoid risky APIs.

• Note compliance items (e.g., “meets logging/auth policy; no PII in logs”).

• Let gates block on violations (coverage threshold, secret leaks, OWASP patterns).

Automated code review tools catch most violations early; your note gives reviewers context.

7. Make it easy to verify

• Provide run/rollback notes, sample data, and migration commands if needed.

• List impacted modules and monitoring/alerting you expect to watch after release.

This reduces reviewer guesswork and improves productivity in engineering during rollout.

Bottom line: An author who ships a clean diff, clear context, and passing checks enables a faster, deeper code review. Pair human discipline with AI code review prechecks (CodeAnt AI Developer 360) to automate the trivial and spotlight real risks, so reviewers spend their time on architecture, correctness, and maintainability, not nits.

Keep Code Reviews Small and Manageable

One of the golden rules of code review best practices is simple: less is more. Reviewers catch far more issues when pull requests are small and focused. Studies show that reviewing more than 200 to 400 lines of code at a time sharply reduces defect detection rates, after that, focus and accuracy drop dramatically.

Large pull requests do more than tire reviewers, they slow the entire code review process. Reviewers hesitate to start, context-switching gets harder, and merge velocity drops. Teams using AI code review tools like CodeAnt AI often see 2-3x faster code review turnaround because PRs are analyzed, summarized, and flagged for key issues automatically.

Why smaller reviews win

• Better focus: Reviewers stay sharp and spot subtler bugs.

• Faster turnaround: Small PRs feel approachable, so reviewers respond quicker.

• Higher quality feedback: It’s easier to reason about the design, logic, and edge cases when the diff is limited.

• Less rework: Small, iterative feedback cycles prevent major rewrites later.

Practical guidelines for keeping reviews manageable

1. Target under 400 LOC per review: ideally under 300. Beyond that, cognitive fatigue sets in.

2. Break large features into smaller pull requests: for example, backend API in one PR, UI layer in another, tests in a follow-up.

3. Timebox review sessions: cap reviews to about 60 minutes. After that, attention and defect-finding ability drop.

4. Use incremental reviews: If a feature can’t be split, review in multiple passes, architecture first, then logic, then tests.

5. Encourage reviewers to push back: If a PR is too large, ask for it to be broken down before review starts.

Speed meets quality

Smaller, faster reviews improve developer productivity and DORA metrics like lead time for changes. Google’s engineering research found that when code quality improves through structured reviews, overall team velocity rises (research.google).

In other words: small PRs make better software, and faster teams. By combining small, focused pull requests with automation from AI code review tools, your team can achieve faster merges, fewer bugs, and a consistently healthy codebase.

Focus on the Big Picture: Architecture, Logic, and Quality

During a software code review, spend human attention where it creates real value. The goal of the code review process isn’t perfect commas, it’s shipping robust, maintainable code that meets requirements and improves long-term health.

1. Correctness and functionality

Does the code do what it’s supposed to do? Verify the logic against the acceptance criteria or design docs. Look for any bugs or logic errors, off-by-one mistakes, incorrect calculations, unhandled cases, etc. Run the code or tests if needed. Make sure edge cases are handled (empty inputs, error conditions, unusual scenarios). If you spot a potential bug, call it out with evidence (e.g. “given X input, this function might throw an error, did we consider that?”).

2. Design and architecture

Is the solution implemented in a reasonable way? Check if the code follows your system’s architectural guidelines and principles of good design (e.g. separation of concerns, DRY, SOLID principles). Watch for unnecessary complexity. Could this be simpler or more consistent with the existing codebase? If you see duplicated code or an opportunity to use a common utility, point it out. The code should be maintainable and fit well with the overall system design.

3. Readability and maintainability

Code is read far more often than it’s written. Ensure names of variables, functions, and classes are self-explanatory. Is the code easy to understand at a glance? If not, suggest refactoring complex routines into smaller, well-named functions. Check for proper comments where needed (e.g. non-obvious logic), but also ensure the code isn’t commented-out or cluttered unnecessarily. Consistent coding style and clear structure greatly improve maintainability. If something confuses you as a reviewer, leave a friendly comment, chances are others will find it confusing too.

Source: https://www.ijrte.org/wp-content/uploads/papers/v12i2/B76660712223.pdf 

4. Test coverage and error handling

Verify that new code comes with appropriate tests. Are all critical paths and edge cases covered by tests? If not, request additional tests (for example, “Please add a unit test for the case when X is null”). Also consider how the code handles failures: Does it log errors? Does it propagate exceptions or handle them gracefully? Part of writing quality code is anticipating what can go wrong. Encourage the author to add checks or fallback logic if you see a scenario that’s not handled (e.g. “What if the API call fails? Should we retry or alert the user?”).

5. Security and compliance

Don’t overlook security in code reviews. Even if you’re not a security engineer, keep an eye out for common vulnerabilities. Are inputs validated and sanitized (to prevent SQL injection, XSS, etc.)? Does the code avoid hardcoding secrets or credentials (which should be in config)? If the change involves auth, encryption, or sensitive data, ensure it follows security best practices or compliance requirements (for instance, using parameterized queries, proper encryption libraries, etc.). It’s helpful to maintain a simple security checklist for code reviews. By flagging potential security issues in PRs, you catch them early when they’re cheapest to fix. (Note: modern code scanners or AI assistants can help automatically detect many security flaws, more on that later.)

Related read: 

Give Code Review Feedback Without Pushback (2025) 

By focusing on these high-impact areas, code reviews fulfill their purpose as a quality gate. As one study puts it, code review’s value is in catching logic errors, design issues, and missing tests – things only humans can spot – while improving the overall code health of the project. In short, spend human effort where it counts, and let tools handle the trivial checks.

Provide Constructive Feedback (and Avoid Nit-Picking)

How feedback is delivered in a code review is as important as what is said. The goal is to improve the code and educate the team, not to assert dominance or nit-pick every tiny flaw. Always keep comments professional, respectful, and focused on the code, never the person. Here are some best practices for reviewer feedback:

1. Start with positives

A little praise goes a long way. If you notice a clever solution or a well-written test, call it out. For example: “Nice job handling the caching logic here, that will save a lot of requests.” Starting with a positive tone shows you appreciate the author’s effort and sets a collaborative mood. Every piece of code has something that can be acknowledged, even if small.

2. Be specific and actionable 

Avoid vague critiques like “this is confusing” or “bad code.” Instead, pinpoint the issue and, if possible, suggest a solution. For instance: instead of saying “This function is terrible,” you might say “This function is doing a lot, maybe split it into two, one for validation and one for processing, to make it easier to understand.” Concrete suggestions help the author understand exactly what to do next.

3. Ask questions rather than issuing orders

Adopt a curious tone. Phrasing feedback as questions can be less confrontational: “Have you considered using a standard library sort here instead of a custom implementation? It might simplify things.” This invites discussion and lets the author explain their thinking. Often, you might learn there was a reason for the approach, or the question prompts them to see a new alternative. It turns the review into a dialogue rather than a list of demands.

4. Frame it as teamwork (“we”), not blame

Use inclusive language. For example, “We need to ensure this service handles timeouts, maybe we can add a retry here.” This makes it clear the reviewer and author share the same goal (quality code) and are solving it together. Avoid personal language (“your code… you forgot…”) which can come off as accusatory. 

Assume positive intent, most likely the author did what made sense to them; it’s the code that needs improvements, not their character.

5. Avoid excessive nit-picking

If you notice minor issues (typos, tiny style issues) that are not worth a drawn-out discussion, you can still mention them but mark as nit or non-blocking. For example: “nit: consider renaming this variable for clarity.” This tells the author it’s an optional suggestion. However, try not to overload your review with a dozen nits about trivial matters, that can frustrate the author and obscure the important feedback. Remember, if your team has automated formatting or linting, many nits should already be caught by tools. Focus your energy on more meaningful feedback.

6. Keep tone and context positive

Back your comments with data, examples, or documentation. For instance, cite a failing test, reference a style-guide rule, or link to a past issue. When a developer improves in later PRs, acknowledge it, “Cleaner implementation this time, nice work!” Recognition strengthens review culture and reinforces good habits.

(As a side note, it’s beneficial to recognize improvements in subsequent reviews. If an author incorporates feedback from last time, acknowledge it: “Much cleaner approach this time around!” This reinforces good practices and builds trust.)

Leverage AI Code Review Tools for Automation

Manual reviews are vital, but human attention should be reserved for what machines can’t do: understanding context, design, and intent. The smartest teams use automation and AI code review tools to handle repetitive checks before a reviewer ever opens the pull request.

1. Linters and static analysis

These tools automatically check code for stylistic consistency, potential bugs, and common anti-patterns. Set up linters for your language (ESLint, Pylint, etc.) and static analysis tools to run on each PR. 

For instance, they can catch unused variables, deprecated API calls, thread-safety issues, etc. Many teams integrate these into CI pipelines so that if the code has lint errors or simple bugs, the PR is red-flagged before review.

2. Automated tests and CI

Every PR should trigger your continuous integration tests. If tests fail, reviewers should ideally hold off until the author fixes the build. A culture of “green builds” ensures you’re not wasting time reviewing code that doesn’t pass basic functionality checks. 

Also consider coverage reports, if a PR lowers code coverage significantly, that’s a sign to add tests. (Some tools or AI assistants will even remind you of missing tests.)

3. Pre-merge checks for security and compliance

Especially in high-stakes software (fintech, healthcare, etc.), consider automated security scans on each PR. SAST tools can detect vulnerabilities (e.g. OWASP top 10 issues) in code. Dependency scanners can alert you if the code introduces a library with known vulnerabilities. 

If compliance standards (PCI, HIPAA, SOC2) are in play, you can encode some of those checks as well (for example, ensure encryption is used for sensitive data). Automation here acts as another reviewer that never gets tired.

4. Modern code review platforms and AI assistants

In 2025 and beyond, teams are increasingly adopting AI-powered code review tools to boost productivity. It understands context, patterns, and intent. Platforms like CodeAnt AI combine machine learning, static analysis, and LLM reasoning to:

• Summarize diffs and explain what changed in plain English.

• Highlight potential bugs, complexity spikes, and security risks.

• Suggest one-click fixes or refactors directly inside the PR.

• Reduce manual review effort by up to 80% while maintaining consistent standards.

Unlike legacy scanners, CodeAnt provides a unified view across code quality, security, and developer productivity metrics, eliminating the need for multiple add-ons. Teams using AI assistance report faster merge cycles and drastically fewer post-release defects.

5. Focus human attention where it counts

By the time a human reviewer steps in, the grunt work should be done. Linters catch syntax. CI ensures tests pass. AI points out vulnerabilities, duplication, or untested logic. The reviewer can now focus on architecture, maintainability, and long-term quality, the creative side of engineering that no algorithm can replace.

Automating these steps not only boosts developer productivity but also standardizes quality across teams. With AI tools like CodeAnt, your organization enforces consistent standards, accelerates review cycles, and ensures every line of code meets both human and machine-level scrutiny before it ships.

Track Code Review Metrics to Continuously Improve

You can’t improve what you don’t measure. High-performing engineering organizations treat code reviews as an iterative process to be refined. By tracking developer productivity metrics around code reviews, you can identify bottlenecks and areas to optimize in your workflow. Some useful metrics and practices include:

1. Review turnaround time

Measure how long it takes for a pull request to get its first review and how long it takes to merge. Long waits create delivery drag and context loss.

• Set SLAs: Agree on a 24-hour window for initial feedback.

• Monitor response times: A growing backlog signals review overload or imbalanced workloads.

• Balance assignments: Distribute reviews evenly so senior engineers don’t become bottlenecks.

Faster, consistent feedback loops keep developers productive and reduce context-switch fatigue.

2. Inspection rate and defect rate

These traditional code inspection metrics gauge efficiency. Inspection rate measures how many lines of code a team reviews per hour. If this is extremely low, your reviews might be too painstaking (or your code too complex). 

Defect rate is how many issues are found per hour of review, if this drops, maybe reviews are getting superficial or code quality is improving. While you need not obsess over these numbers, they provide a quantitative backdrop to your intuition.

3. Defect density

Track how many bugs are caught during reviews vs. those escaping to testing or production.

• A high capture rate means reviews are catching issues early.

• Frequent post-merge defects indicate review blind spots or fatigue.

Integrating AI-assisted reviews via CodeAnt AI helps teams detect deeper logic and security issues before release, reducing production bug density dramatically.

4. Coverage of reviews

Ensure every meaningful change goes through review. Unreviewed merges, even “small” ones, can erode trust in the pipeline.

• Make “peer review completed” part of your Definition of Done.

• Automate enforcement with CI rules (e.g., no merge without one approval).

• Exception: emergency fixes, followed by a retrospective review.

5. Team metrics and knowledge sharing

Some more advanced metrics: How many different people review a given team’s code (cross-pollination vs silos)? Does each developer both give and receive roughly a balanced amount of code review feedback? Uneven patterns might indicate that senior engineers are reviewing everything (risking burnout) or conversely, that knowledge isn’t spreading enough. Encouraging a healthy rotation of reviewers grows collective code ownership.

6. Dashboards and insights

Modern platforms like CodeAnt AI’s Review Insights Dashboard visualize all these metrics in one place. You can see:

• PR turnaround time by repo or team

• Average PR size vs. merge speed

• Defect patterns and coverage trends

If a repository consistently has slow reviews or high defect density, that’s a signal, not to blame individuals, but to simplify code, redistribute load, or add automation.

7. Continuous improvement loop

Hold short retrospectives specifically on code review effectiveness:

• What issues are being missed?

• Are reviews too slow or too nitpicky?

• Is the workload fairly distributed?

Use insights from your metrics to tweak processes and update review checklists. Over time, this builds a data-driven feedback loop where every sprint’s reviews inform the next, making your software code review culture faster, smarter, and more consistent.

Build a Positive, Learning-Oriented Code Review Culture

Great code reviews are about people first. The fastest path to better reviews, higher developer productivity, and a healthier codebase is a culture where reviewing is a shared craft, not a punitive audit. Here’s how to build that mindset:

Set the tone: growth over judgment

Make it explicit that coding review is not a performance evaluation. Everyone’s work, including seniors, gets reviewed because the goal is good code, shared context, and resilient systems. Celebrate reviews that prevented defects, improved design, or taught a new technique. This reinforces good code review practices and reduces defensiveness.

Coach, don’t criticize

Treat every comment as an opportunity to mentor. Frame feedback around outcomes and reasoning (“why”), not people. Invite discussion (“What do you think about extracting this?”) to model how to perform a code review that teaches, not lectures. Over time, this raises software developer productivity because engineers learn faster from real changes.

Flatten the feedback loop

Empower juniors to review code from seniors. Fresh eyes catch assumptions; it also builds confidence and distributes knowledge. Rotate code reviewers so expertise spreads and silos shrink, key to sustainable productivity in engineering.

Recognize the invisible work

Reviewing is real engineering. Give shout-outs in standups or retros for insightful reviews, crisp PR descriptions, or reviewers who unblocked merges. Tracking lightweight developer metrics (e.g., time-to-first-review, review depth) helps leadership see and reward the effort that powers velocity.

Agree on house rules

Codify code review best practices: what’s blocking vs. a “Nit:”, expected tone, when to escalate, and how to resolve stalemates with data or style guides. Clear norms reduce friction and speed decisions, exactly what makes a good code review culture durable.

Let tools remove friction

Use code review tools and AI code review to automate nits (lint, style, simple bugs) so humans focus on architecture, logic, and maintainability. When automation handles the trivial, human discussions stay high-signal and morale stays high.

Keep disagreements professional and evidence-based

When opinions diverge, return to principles, tests, and documented standards. If needed, hop on a quick call, then summarize the decision in the PR. This keeps momentum without bruising egos and models how to do code reviews like a team sport.

Bottom line: a respectful, learning-focused review of culture compounds. It boosts developer productivity, shortens lead time, and produces consistently better reviews, because people feel safe to ask, suggest, and improve together. That’s the hallmark of a high-performing engineering org and the foundation for any AI-augmented, modern code review practice.

Better Code, Faster Deployments with CodeAnt AI Code Review Tool

Great code reviews are not a formality; they are a system. When teams apply clear code review best practices, they build cleaner, more reliable software and stronger engineering culture.

What to keep doing:

• Set clear guidelines and follow a simple checklist.

• Keep pull requests small, focused, and well-described.

• Spend reviewer time on architecture, logic, and security, not minor formatting.

• Track review speed, PR size, and defect capture to improve team performance.

The real unlock comes when you combine human judgment with automation. Machines handle the repetitive checks, while people focus on reasoning and design. That’s where AI code review truly adds value.

Why high-performing teams use CodeAnt AI:

• Automates style, duplication, and simple bug detection before reviews start.

• Highlights real issues with clear summaries and one-click fixes.

• Enforces security, coverage, and compliance policies consistently.

• Delivers live developer productivity metrics and DORA insights that pinpoint bottlenecks.

Start with CodeAnt AI today for 14-days FREE. Turn every code review into progress. Ship with confidence. Build with CodeAnt AI.