Code Review Best Practices Cheat Sheet

Updated 2026-05-28

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Code review is a systematic software quality assurance technique where developers examine each other's code changes before merging them into the main codebase. Originating from formal inspection processes in the 1970s and evolving through pair programming and modern pull request workflows, code reviews now serve as the linchpin of collaborative software development—catching bugs early, spreading knowledge across teams, and maintaining code health over time. The practice has become so fundamental that research shows teams practicing thorough code review ship 15% fewer bugs to production, yet the difference between effective and performative review often comes down to a handful of non-obvious principles: understanding when to block versus when to nitpick, how cognitive load affects defect detection rates, and why smaller PRs paradoxically receive more substantive feedback than large ones. In 2026, teams must also grapple with reviewing AI-generated code, which introduces distinct failure modes that traditional review habits weren't designed to catch.

What This Cheat Sheet Covers

This topic spans 21 focused tables and 198 indexed concepts, 126 flashcards. Below is a complete table-by-table outline of this topic, spanning foundational concepts through advanced details.

Table 1: Author Pre-Submission ChecklistTable 2: Reviewer Mindset and ApproachTable 3: Review Focus AreasTable 4: Pull Request Size GuidelinesTable 5: Conventional Comment PrefixesTable 6: Automated vs. Manual ReviewTable 7: Code Review Workflow StagesTable 8: Merge StrategiesTable 9: Review Turnaround Time and SLATable 10: Handling Disagreements and EscalationTable 11: Anti-Patterns to AvoidTable 12: Security-Focused Review ChecklistTable 13: Mentoring Junior Developers Through Code ReviewTable 14: Async and Remote Code Review Best PracticesTable 15: Code Review MetricsTable 16: Writing Effective PR DescriptionsTable 17: Review Focus by Code AreaTable 18: Code Review Terminology and AbbreviationsTable 19: CODEOWNERS and Code OwnershipTable 20: AI-Assisted Code Review ToolsTable 21: Reviewing AI-Generated Code

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Table 1: Author Pre-Submission Checklist

Before opening a pull request, authors carry significant responsibility for reducing the reviewer's cognitive load. A well-prepared PR respects reviewers' time, increases the likelihood of substantive feedback, and dramatically shortens the review cycle.

Action	Example	Description
Self-review first	Read your own diff in GitHub as if you're the reviewer before requesting review	• Mandatory first step—catch obvious issues, add inline comments explaining complex logic, and verify tests pass • saves reviewer time and demonstrates professionalism
Keep PRs small	Target 200-400 lines changed; split large features into logical increments	• Research shows defect detection drops dramatically above 400 lines • reviewers maintain focus better on smaller changes, leading to faster approvals and fewer bugs
Write clear PR descriptions	What changed, why it changed, how to test; link related tickets; include screenshots for UI changes	• Context is everything—reviewers should understand the business reason and approach without reading code first • reduces back-and-forth questions
Run automated checks locally	`pre-commit run --all-files` before pushing; verify linters, formatters, and tests pass	• Automation before human review—never waste reviewer time on lint errors or failing tests • use pre-commit hooks to enforce this automatically
Annotate complex logic	Add inline PR comments explaining why you chose a specific algorithm or pattern	• Proactively address potential reviewer questions • demonstrates you've considered trade-offs and aren't blindly copying code
Commit message hygiene	`feat: add user authentication` `fix: resolve race condition in payment processing`	Follow Conventional Commits format (`type: description`)—enables automatic changelog generation and helps reviewers understand change intent at a glance
Test coverage included	Add unit tests, integration tests as appropriate; ensure new code paths are tested	• Tests are reviewable documentation—they show expected behavior and prevent regressions • reviewers should verify test quality, not just code quality
Check for secrets and sensitive data	Search for API keys, passwords, tokens; verify `.env` files not committed	• Single most critical security check—accidentally committed secrets can't be truly removed from git history • use tools like git-secrets or truffleHog
Flag AI-generated code	Add PR comment like "Sections X–Y are Copilot/Cursor suggestions" or use AI tagging tools	• AI code requires deeper scrutiny—48% of AI-generated code contains security vulnerabilities (Veracode 2025) • reviewers should apply higher skepticism to AI-assisted logic and tests
Rebase on latest main	`git rebase origin/main` before requesting review; resolve conflicts locally	• Ensures PR is merge-ready and reviewers see the most current context • conflicts discovered during review waste everyone's time
Tag specific reviewers	Request review from code owner + one additional team member; mention specific concerns	• Direct assignment reduces delays—don't rely on team notification • ask for focused feedback on risky areas rather than generic "LGTM"

Table 2: Reviewer Mindset and Approach

The most technically astute reviewer can damage a team's culture if their approach is adversarial. Effective reviewing is as much about tone and framing as it is about technical knowledge—reviewers who master both produce better outcomes for code and for people.

Principle	Example	Description
Assume competence	"What context am I missing?" not "Why did you do it this way?"	• Authors made deliberate choices—frame questions curiosity-first • psychological safety depends on assuming good intent and asking before criticizing

The rest of this section is part of the full cheat sheet

Table 3: Review Focus Areas

Not all code deserves equal scrutiny. Effective reviewers mentally triage what to examine deeply—correctness and security first, style and polish last. Understanding which dimensions to prioritize per change type is what separates insightful reviews from rubber stamps.

Area	Example	Description
Correctness	Does the code do what it claims? Edge cases covered? Off-by-one errors?	• Highest priority—functional bugs harm users directly • verify logic against requirements, test happy path and failure scenarios

The rest of this section is part of the full cheat sheet

Table 4: Pull Request Size Guidelines

PR size is one of the strongest predictors of review quality. Research consistently shows that reviewers maintain deeper focus and find more defects in smaller, focused changes—not because they're trying harder, but because the cognitive load stays manageable.

Guideline	Example	Description
Under 200 lines ideal	Target ~50 lines for perfect focus; under 200 gets fastest approval	Smaller PRs get more substantive comments and merge faster—reviewers don't mentally check out from cognitive overload

The rest of this section is part of the full cheat sheet

Table 1: Author Pre-Submission Checklist

Action	Example	Description
Self-review first	Read your own diff in GitHub as if you're the reviewer before requesting review	• Mandatory first step—catch obvious issues, add inline comments explaining complex logic, and verify tests pass • saves reviewer time and demonstrates professionalism
Keep PRs small	Target 200-400 lines changed; split large features into logical increments	• Research shows defect detection drops dramatically above 400 lines • reviewers maintain focus better on smaller changes, leading to faster approvals and fewer bugs
Write clear PR descriptions	What changed, why it changed, how to test; link related tickets; include screenshots for UI changes	• Context is everything—reviewers should understand the business reason and approach without reading code first • reduces back-and-forth questions
Run automated checks locally	`pre-commit run --all-files` before pushing; verify linters, formatters, and tests pass	• Automation before human review—never waste reviewer time on lint errors or failing tests • use pre-commit hooks to enforce this automatically
Annotate complex logic	Add inline PR comments explaining why you chose a specific algorithm or pattern	• Proactively address potential reviewer questions • demonstrates you've considered trade-offs and aren't blindly copying code
Commit message hygiene	`feat: add user authentication` `fix: resolve race condition in payment processing`	Follow Conventional Commits format (`type: description`)—enables automatic changelog generation and helps reviewers understand change intent at a glance
Test coverage included	Add unit tests, integration tests as appropriate; ensure new code paths are tested	• Tests are reviewable documentation—they show expected behavior and prevent regressions • reviewers should verify test quality, not just code quality
Check for secrets and sensitive data	Search for API keys, passwords, tokens; verify `.env` files not committed	• Single most critical security check—accidentally committed secrets can't be truly removed from git history • use tools like git-secrets or truffleHog
Flag AI-generated code	Add PR comment like "Sections X–Y are Copilot/Cursor suggestions" or use AI tagging tools	• AI code requires deeper scrutiny—48% of AI-generated code contains security vulnerabilities (Veracode 2025) • reviewers should apply higher skepticism to AI-assisted logic and tests
Rebase on latest main	`git rebase origin/main` before requesting review; resolve conflicts locally	• Ensures PR is merge-ready and reviewers see the most current context • conflicts discovered during review waste everyone's time
Tag specific reviewers	Request review from code owner + one additional team member; mention specific concerns	• Direct assignment reduces delays—don't rely on team notification • ask for focused feedback on risky areas rather than generic "LGTM"

Table 2: Reviewer Mindset and Approach

Principle	Example	Description
Assume competence	"What context am I missing?" not "Why did you do it this way?"	• Authors made deliberate choices—frame questions curiosity-first • psychological safety depends on assuming good intent and asking before criticizing

Table 3: Review Focus Areas

Area	Example	Description
Correctness	Does the code do what it claims? Edge cases covered? Off-by-one errors?	• Highest priority—functional bugs harm users directly • verify logic against requirements, test happy path and failure scenarios

Table 4: Pull Request Size Guidelines

Guideline	Example	Description
Under 200 lines ideal	Target ~50 lines for perfect focus; under 200 gets fastest approval	Smaller PRs get more substantive comments and merge faster—reviewers don't mentally check out from cognitive overload