Code reviews are one of the most effective ways to catch bugs, improve code quality, and share knowledge across a team. But without a structured approach, important issues can slip through.
This comprehensive checklist will help you conduct thorough, effective code reviews that catch problems before they reach production.
🔒 Security
Input Validation
- ✅ All user input is validated and sanitized
- ✅ SQL queries use parameterization (no string concatenation)
- ✅ File uploads are restricted by type and size
- ✅ URLs and redirects are validated against allowlists
Authentication & Authorization
- ✅ Authentication is required for protected routes
- ✅ Authorization checks verify user permissions
- ✅ Sessions expire after inactivity
- ✅ Password policies are enforced
- ✅ Sensitive operations require re-authentication
Data Protection
- ✅ Passwords are hashed with bcrypt/argon2 (not MD5/SHA1)
- ✅ Sensitive data is encrypted at rest
- ✅ HTTPS is enforced for all connections
- ✅ API keys and secrets are not hardcoded
- ✅ Logs don't contain sensitive information
Common Vulnerabilities
- ✅ No SQL injection possibilities
- ✅ XSS prevention (proper escaping/sanitization)
- ✅ CSRF protection implemented
- ✅ No path traversal vulnerabilities
- ✅ Rate limiting on sensitive endpoints
🐛 Functionality
Logic & Correctness
- ✅ Code does what it's supposed to do
- ✅ Edge cases are handled
- ✅ Error conditions are properly managed
- ✅ Null/undefined checks where necessary
- ✅ Off-by-one errors are avoided
Testing
- ✅ Unit tests cover new functionality
- ✅ Integration tests verify component interaction
- ✅ Test coverage meets project standards (80%+)
- ✅ Tests are meaningful (not just for coverage)
- ✅ All tests pass before merging
⚡ Performance
Database
- ✅ N+1 query problems are avoided
- ✅ Indexes exist for frequently queried columns
- ✅ Large datasets are paginated
- ✅ Queries are optimized (no SELECT *)
- ✅ Transactions are kept short
API & Network
- ✅ Unnecessary API calls are eliminated
- ✅ Results are cached where appropriate
- ✅ Large responses are compressed
- ✅ Connection pooling is used
- ✅ Timeouts are configured
Code Efficiency
- ✅ No unnecessary loops or iterations
- ✅ Complex operations are optimized
- ✅ Memory leaks are prevented
- ✅ Large files/data are streamed not loaded entirely
- ✅ Lazy loading is used where beneficial
📖 Readability & Maintainability
Code Style
- ✅ Follows project coding standards
- ✅ Consistent naming conventions
- ✅ Proper indentation and formatting
- ✅ No dead code or commented-out blocks
- ✅ Linter passes without warnings
Clarity
- ✅ Functions have clear, single responsibilities
- ✅ Variable names are descriptive
- ✅ Complex logic is explained with comments
- ✅ Magic numbers are replaced with named constants
- ✅ Code is self-documenting where possible
Structure
- ✅ Files are organized logically
- ✅ Functions are reasonably sized (<50 lines)
- ✅ Classes follow SOLID principles
- ✅ Dependencies are minimal and justified
- ✅ No circular dependencies
📚 Documentation
- ✅ Public APIs are documented
- ✅ Complex algorithms have explanations
- ✅ README is updated if needed
- ✅ Breaking changes are noted
- ✅ Migration guides provided for major changes
🔄 Dependencies
- ✅ New dependencies are necessary and well-maintained
- ✅ Dependency licenses are compatible
- ✅ No known security vulnerabilities
- ✅ Bundle size impact is acceptable
- ✅ Alternatives were considered
🎯 Best Practices
For Reviewers
- Be constructive: Suggest improvements, don't just criticize
- Ask questions: "Why did you choose this approach?"
- Provide examples: Show better alternatives
- Prioritize: Mark critical issues vs. nits
- Be timely: Review within 24 hours
For Authors
- Keep PRs small: <400 lines of code
- Self-review first: Catch obvious issues before submitting
- Add context: Explain why, not just what
- Respond promptly: Address feedback quickly
- Stay humble: Reviews help you improve
Automated Checks
Don't waste human time on things machines can check. Automate:
- Linting: ESLint, Pylint, RuboCop
- Formatting: Prettier, Black, gofmt
- Type checking: TypeScript, mypy
- Test coverage: Jest, pytest-cov
- Security scanning: Snyk, Bandit, or ScanMyCode.dev
# Example GitHub Actions workflow
name: Code Quality
on: [pull_request]
jobs:
quality:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v3
- name: Lint
run: npm run lint
- name: Type check
run: npm run type-check
- name: Test
run: npm test -- --coverage
- name: Security scan
run: npm auditCommon Code Review Anti-Patterns
❌ Rubber Stamping
Approving PRs without actually reviewing them. This defeats the entire purpose.
❌ Nitpicking
Focusing only on minor style issues while missing critical logic bugs.
❌ Design Debates
Major architectural discussions should happen before code is written, not during review.
❌ Blocking on Preferences
Don't block PRs over personal style preferences. Use linters for consistency.
❌ Review Fatigue
PRs with 1000+ lines of changes rarely get thorough reviews. Keep them small.
Tools to Enhance Code Reviews
GitHub/GitLab Features
- Code suggestions for quick fixes
- Review threads for focused discussions
- Required approvals before merge
- CODEOWNERS for automatic reviewer assignment
AI-Assisted Reviews
AI tools can help catch issues humans might miss:
- GitHub Copilot: Inline suggestions as you code
- CodeRabbit: Automated PR review comments
- ScanMyCode.dev: Comprehensive pre-merge security + quality audit
When to Use Automated Audits
While human code reviews are essential for understanding intent and design, automated audits excel at:
- Pre-release security checks
- Large refactoring reviews
- Legacy code assessment
- Third-party code evaluation
- Comprehensive performance analysis
Conclusion
Effective code reviews combine human judgment with automated tools. Use this checklist as a guide, but adapt it to your team's needs and tech stack.
For comprehensive automated audits that complement your code review process, try ScanMyCode.dev. Get AI-powered analysis covering security, performance, and code quality in 24 hours.