Code Review Checklist: 40 Questions Before You Approve

A well-structured code review checklist touches every pillar of modern software engineering: design, functionality, security, testing, readability, documentation, performance, scalability, and process quality. Incorporating this checklist into every pull-request inspection consistently uncovers defects that would cost 10-100× more to fix after production release.

Microsoft, Google, and OWASP data all echo the same warning: teams that rely on ad-hoc reviews miss critical defects because reviewers gravitate toward easy cosmetic comments. By contrast, using a formal code review checklist keeps the focus on logic, edge cases, and security vulnerabilities while automated tools handle formatting.

The Hidden Cost of Skipping a Code Review Checklist

Microsoft Research reports that only about 15% of code-review comments address real defects, while the bulk target style or formatting that automated linters could fix. That misallocation of effort dramatically raises bug-fix costs later in the lifecycle. Adding a formal code review checklist ensures reviewers focus on logic, edge cases, and security, not cosmetic nit-picks.

Industry data confirms the stakes: resolving a bug during review is dramatically cheaper than correcting it in production. Yet many teams still rely on ad-hoc intuition, which varies by fatigue, expertise, and schedule pressure. Google's Engineering Practices, as publicly documented, do not state that reviewers should approve a change list (CL) when it "definitely improves the overall code health," even if it isn't perfect. A concise but comprehensive checklist makes that mandate practical.

When teams add Augment Code's Context Engine to their code review checklist process, they identify breaking changes 5-10× faster because the engine maps dependencies across 400 K+ files. Microsoft's Code-with-Engineering Playbook and Google's guidelines offer guidance on code reviews: Google's code review guidelines ask reviewers to ensure each contribution fits the system design and doesn't introduce unnecessary complexity, but Microsoft's Code-with-Engineering Playbook does not explicitly recommend that changes be self-contained.

Augment Code's Context Engine analyzes code relationships across 400 K+ files to flag breaking changes during review.

Logic and Correctness Questions (1-10) for Your Code Review Checklist

Academic research from arXiv shows that off-by-one errors, null dereferences, and type-conversion mistakes routinely slip through unstructured reviews. These first 10 code review checklist questions address those gaps. Teams looking to automate bug detection can supplement manual review with AI-powered analysis.

Question 1: Does the code correctly implement all stated business requirements?

Verify operator precedence, overflow checks, edge cases, and complete requirement coverage.

Question 2: Are loop boundaries correct?

Confirm < vs <=, zero- vs one-based indexing, and empty/maximum scenarios.

Question 3: What happens if this variable or object is null at runtime?

Ensure nullable returns are checked, DB results validated, and errors handled safely.

Question 4: Are all user inputs validated comprehensively?

Check type, length, format, range, server-side enforcement, and Unicode or encoding edge cases.

Question 5: What implicit assumptions does this code make?

Document scale expectations, data shape, and concurrency assumptions.

Question 6: Could thread interleaving cause unexpected behavior?

Look for race conditions, deadlocks, lock-ordering issues, and check-then-act bugs.

Question 7: Are shared resources protected by appropriate synchronization?

Review mutex usage, lock-free structures, or actor isolation.

Question 8: What happens when this operation fails midway through?

Verify resource cleanup, consistent state, and secure error logging.

Question 9: How does the code handle time-related edge cases?

Consider time zones, DST shifts, leap years/seconds, and clock skew.

Question 10: Can the system reach invalid states through unexpected operation sequences?

Validate state-machine transitions, idempotency, and retry safety.

Security Questions (11-20) Aligned With OWASP

Systematically apply the OWASP Top 10:2025 taxonomy throughout these code review checklist items. For comprehensive security scanning, consider AI-powered code security tools that trace vulnerabilities across your codebase.

Question 11: Are all user inputs validated and sanitized before SQL, OS, or LDAP use?

Require parameterized queries and strict length/format checks.

Question 12: Are passwords and credentials stored with strong hashing (bcrypt, Argon2, PBKDF2)?

Ban plaintext or weak hashes (MD5, SHA-1).

Question 13: Is authorization checked on every request, including direct object references?

Enforce server-side permission validation.

Question 14: Is sensitive data encrypted in transit (TLS 1.2+) and at rest?

Confirm robust key management and rotation.

Question 15: Are security headers (CSP, HSTS, X-Frame-Options, X-Content-Type-Options) configured?

Verify HTTP response headers prevent clickjacking, MIME-sniffing, and enforce HTTPS across all endpoints.

Question 16: Is user data encoded/escaped for its rendering context (HTML, JS, CSS, URL)?

Apply context-appropriate encoding to prevent XSS attacks when displaying user-generated content.

Question 17: Are external dependencies scanned for known vulnerabilities and integrity?

Use automated dependency scanning tools and verify package checksums before deployment.

Question 18: Are security-relevant events logged with adequate, but non-sensitive, detail?

Log authentication attempts, authorization failures, and data access without exposing PII or credentials.

Question 19: Are user-supplied URLs validated with allowlists to prevent SSRF?

Restrict outbound requests to approved domains and block internal IP ranges and metadata endpoints.

Question 20: Are anti-CSRF tokens present on every state-changing request?

Implement synchronizer tokens or double-submit cookies on all POST, PUT, and DELETE operations.

Augment Code traces user input flow end-to-end, enabling full OWASP coverage during review.

Performance Questions (21-30) to Keep Your Code Fast

Many production slowdowns stem from issues that could have been spotted with a performance-oriented code review checklist. Teams managing large codebases benefit from AI performance tools that analyze patterns at scale.

Question 21: Does this code execute queries inside a loop?

Identify N+1 query patterns that multiply database round-trips and degrade response times.

Question 22: Can multiple DB calls be combined into one bulk or JOIN query?

Consolidate separate queries into batch operations or joins to reduce network latency.

Question 23: Are all allocated resources released in every code path?

Verify file handles, connections, and streams close properly in success, error, and exception paths.

Question 24: Do any in-memory collections grow without bounds?

Check for missing size limits on caches, queues, and buffers that could cause memory exhaustion.

Question 25: Are unnecessary object allocations happening in hot paths?

Reduce garbage collection pressure by reusing objects or using primitives in frequently executed code.

Question 26: Is the algorithmic complexity acceptable for expected data volumes?

Evaluate whether nested iterations or recursive calls will scale acceptably with production data volumes.

Question 27: Could repeated calculations be cached?

Identify expensive computations with stable inputs that benefit from memoization or lookup tables.

Question 28: Could linear searches be replaced with indexed lookups?

Replace O(n) list scans with hash maps, sets, or database indexes for frequently accessed data.

Question 29: Are synchronization bottlenecks limiting horizontal scale?

Review lock contention, shared state, and serialization points that prevent parallel execution.

Question 30: Does the code make blocking calls that could be asynchronous?

Convert synchronous I/O operations to async patterns to improve throughput and resource utilization.

The following example demonstrates the N+1 query antipattern (Question 21) and its optimized solution using a JOIN (Question 22).

# Antipattern: N+1 Query Problem, 101 DB calls
orders = database.query("SELECT * FROM orders")
for order in orders:
    customer = database.query("SELECT * FROM customers WHERE id = ?", order.customer_id)

# Optimized: Single JOIN query
results = database.query("""
    SELECT o.*, c.* 
    FROM orders o 
    LEFT JOIN customers c ON o.customer_id = c.id
""")

Augment Code's Context Engine detects N+1 patterns with 70.6% accuracy on SWE-bench benchmarks.

Maintainability Questions (31-40) for Long-Term Code Health

Google's Engineering Practices center on incremental improvement of code health. These final code review checklist questions reinforce that goal. For systematic approaches to improving code structure, explore essential refactoring techniques.

Question 31: Does this change improve overall code health, even if imperfect?

Accept incremental improvements that make the codebase better, even when the change is not ideal.

Question 32: Are names meaningful and intention-revealing?

Choose variable, function, and class names that communicate purpose without requiring comments.

Question 33: Are functions small, single-purpose, and at one abstraction level?

Keep functions focused on a single task and avoid mixing high-level logic with low-level details.

Question 34: Do function names clearly describe behavior without peeking at implementation?

Ensure callers can understand what a function does from its signature alone.

Question 35: Do comments explain "why," not "what," and is dead code removed?

Reserve comments for intent and rationale; delete unreachable or obsolete code.

Question 36: Is related code kept together with clear vertical separation?

Group logically connected code and use whitespace to distinguish unrelated sections.

Question 37: Does this change reduce coupling and increase flexibility?

Minimize dependencies between modules to enable independent changes and easier testing.

Question 38: Are errors handled explicitly with useful, secure messages?

Provide actionable error information to users and logs without leaking sensitive details.

Question 39: Does the change include tests verifying behavior?

Require unit or integration tests that confirm the new functionality works as intended. Understanding unit vs integration testing helps teams choose the right approach.

Question 40: Does the code follow established style guidelines consistently?

Adhere to team conventions for formatting, naming, and structure to maintain readability.

Augment Code visualizes hidden dependencies, accelerating the detection of coupling and circular references.

How to Use This Code Review Checklist Effectively

Atlassian cites research advising not reviewing more than 200-400 lines of code at a time to maintain review effectiveness.

1. Risk-Based Prioritization

Not every code change warrants the same scrutiny. Allocate review effort based on the potential impact of defects:

1. Core business logic: emphasize correctness, null safety, and validation.
2. Security-critical code: apply all OWASP-aligned security questions.
3. Performance-sensitive paths: inspect DB, memory, and algorithms.
4. Refactoring work: focus on maintainability and incremental improvement.

This tiered approach ensures high-risk changes receive thorough examination while routine updates move through review efficiently.

2. Automation-First Philosophy

Let CI tools enforce formatting, linting, and basic security scans so human attention targets architecture and logic.

3. Blocking Severity Classification

GitLab's code review documentation recommends classifying review comments by severity to streamline approval decisions. Must-fix items (blocking) include security flaws, functional defects, and requirement violations. Should-consider items cover performance improvements and refactors. Nice-to-have items address stylistic preferences outside established team standards.

What to Do Next

Adopt this code review checklist by first selecting the 10 questions most relevant to your recent production incidents. Expand coverage as your team's defect patterns evolve.

Augment Code's Context Engine analyzes dependencies across 400,000+ files through semantic graph analysis, identifying breaking changes 5-10× faster than manual review while maintaining architectural consistency across enterprise codebases. Explore Context Engine capabilities →

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