Code Review Checklist Template 2025: Everything You Need to Ship Better Code

Code review is one of the highest-leverage activities in software development. Studies consistently show that code reviews catch 60–90% of bugs before they ever hit production — far more than automated testing alone. But without a structured checklist, reviews tend to focus on style nits and miss real problems.

This checklist template is built for teams who want reviews that actually improve code quality, not just rubber stamps.

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Why Checklists Work in Code Review

Mental load during code review is high. You're reading unfamiliar logic, tracking context across multiple files, and evaluating multiple dimensions simultaneously: correctness, performance, security, maintainability. Without a checklist, important categories get skipped — not because reviewers are careless, but because the brain naturally focuses on what's obvious (variable names) and misses what requires deliberate attention (race conditions, input validation).

The checklist below is organized into six categories. Work through them in order — correctness first, style last.

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The Complete Code Review Checklist

1. Correctness

• [ ] Does the code do what the PR description says it does?
• [ ] Are edge cases handled? (empty input, null values, boundary conditions)
• [ ] Does it handle errors gracefully — or does it silently fail?
• [ ] Are there any obvious off-by-one errors in loops or array indexing?
• [ ] Does concurrency introduce race conditions or deadlocks?
• [ ] Are database transactions used correctly? (no partial writes)
• [ ] Is business logic correct, not just technically working?

Red flag patterns to check:

• catch(e) {} — swallowed exceptions
• Unchecked array access without bounds validation
• Missing await on async calls

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2. Security

• [ ] Is user input validated and sanitized before use?
• [ ] Are SQL queries parameterized? (no string concatenation with user data)
• [ ] Are secrets or credentials hardcoded anywhere? (API keys, passwords)
• [ ] Is authentication checked before accessing protected resources?
• [ ] Is authorization enforced — not just "is the user logged in?" but "can this user do this?"
• [ ] Are file paths validated to prevent path traversal attacks?
• [ ] Is sensitive data (passwords, tokens, PII) logged anywhere?
• [ ] Are dependencies introduced by this PR known-good? (no obviously suspicious packages)

Tool to use: DevPlaybook AI Code Review — paste your diff and get automated security analysis before human review.

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3. Performance

• [ ] Are there N+1 query patterns? (loop that triggers a database query each iteration)
• [ ] Are expensive operations cached where appropriate?
• [ ] Does the code handle large inputs without memory issues?
• [ ] Are there unnecessary re-renders or re-computations? (frontend)
• [ ] Is pagination used for list endpoints that could return large datasets?
• [ ] Are database indexes used for the queries this code will run?

Performance issues in code review are often subtle. The most common culprit: a loop that queries the database on every iteration.

# Bad: N+1 queries
for user_id in user_ids:
    user = db.query(User).filter(User.id == user_id).first()  # 1 query per iteration
    send_email(user)

# Good: single query
users = db.query(User).filter(User.id.in_(user_ids)).all()
for user in users:
    send_email(user)

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4. Testing

• [ ] Are there tests for the new behavior?
• [ ] Do existing tests still pass? (check CI results)
• [ ] Are edge cases tested, not just the happy path?
• [ ] Are tests testing behavior, not implementation? (don't break if internals change)
• [ ] Is test data realistic? (tests that only pass with id = 1 may fail in production)
• [ ] Is there a test for the bug that was fixed? (regression test)

A useful heuristic: if the PR description says "fix X bug," there should be a test that would have caught the bug before the fix.

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5. Readability and Maintainability

• [ ] Is the code readable by someone unfamiliar with this part of the codebase?
• [ ] Are function and variable names descriptive?
• [ ] Are complex sections explained with comments — not what the code does, but why?
• [ ] Is the code DRY — or is repetition justified?
• [ ] Are magic numbers replaced with named constants?
• [ ] Is the function doing one thing? (single responsibility)
• [ ] Is the diff focused? (unrelated changes mixed in?)

Code diff tool: Use DevPlaybook Code Diff to compare before/after versions of changed files when reviewing locally.

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6. API and Interface Design

• [ ] Does the public API surface make sense? (naming, parameter order, return types)
• [ ] Are breaking changes documented?
• [ ] Is the API consistent with similar patterns in the codebase?
• [ ] Are deprecated functions or parameters flagged?
• [ ] Is the feature flag or rollout strategy defined for risky changes?

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Adapting This Checklist for Your Team

Not all items apply to all PRs. Use this as a starting point and customize:

For small bug fixes: Focus on correctness + regression tests. Skip API design.

For security-sensitive PRs: Expand section 2, require a second reviewer with security expertise.

For performance PRs: Add before/after benchmarks as a PR requirement.

For frontend PRs: Add visual regression tests and accessibility checks (WCAG contrast, keyboard nav).

For refactors: Focus on test coverage and diff scope — refactors should have no behavior changes.

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How to Use This in GitHub

Create .github/PULL_REQUEST_TEMPLATE.md in your repo and paste the checklist. Every new PR will auto-populate with the checklist, and authors can check off items before requesting review:

## What does this PR do?
<!-- Brief description -->

## Checklist

### Author
- [ ] I've tested this locally
- [ ] Edge cases are handled
- [ ] No hardcoded secrets
- [ ] Tests added or updated

### Reviewer
- [ ] Correctness verified
- [ ] Security check done
- [ ] Performance considerations reviewed

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Automate What You Can

Manual code review should focus on logic, design, and context — the things only humans can assess. Automate the mechanical checks:

• Linting: ESLint, Pylint, RuboCop catch style and common bugs automatically
• Security scanning: GitHub's CodeQL, Snyk, or Dependabot for dependency vulnerabilities
• Test coverage: Block PRs that reduce test coverage below a threshold
• Secret detection: git-secrets or GitHub's secret scanning

Use DevPlaybook AI Code Review to generate a first-pass review with automated feedback before your human reviewers look at the code.

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The Non-Technical Part of Code Review

The best technical checklist fails without the right culture. Some rules that make reviews more effective:

For authors:

• Write a clear PR description. Reviewers shouldn't have to read all the code to understand the intent.
• Keep PRs small. A 1,000-line PR gets rubber-stamped. A 150-line PR gets a real review.
• Mark non-obvious choices with a comment. ("I chose this approach because X, considered Y but rejected it because Z.")

For reviewers:

• Review within 24 hours. Slow reviews kill team momentum.
• Distinguish blocking vs. non-blocking comments. ("nit:" for style preferences, no prefix for required changes)
• Ask questions, don't make demands. "What's the reason for this approach?" vs. "Change this."

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Summary

A good code review checklist keeps you from reviewing with your gut. It forces systematic coverage across correctness, security, performance, testing, and readability — the categories most likely to have real defects.

Use this template as-is or adapt it for your team. The goal isn't to complete every checkbox — it's to ensure every category gets deliberate attention before shipping.

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