Code Review with AI: Catching Bugs Before They Ship

The Limits of Human Code Review

Human code reviewers are essential but imperfect. We get tired, we miss edge cases, we skim when we're busy, and we have blind spots. Studies show that even experienced reviewers miss up to 60% of defects in code.

AI doesn't replace human judgment - it augments it. Use AI to catch the mechanical issues so humans can focus on architecture, design, and business logic.

What AI Catches That Humans Miss

Security Vulnerabilities

SQL injection, XSS, path traversal, hardcoded secrets, insecure randomness - AI scans for patterns that humans overlook.

Edge Cases

Empty arrays, null values, race conditions, integer overflow - the edge cases that work fine in testing but fail in production.

Consistency Issues

Naming conventions, error handling patterns, code style - AI remembers the entire codebase and spots deviations.

Performance Problems

N+1 queries, memory leaks, inefficient algorithms, unnecessary re-renders - issues that don't show in small tests.

AI Code Review Workflow

Step 1: Pre-PR Review

Before creating a pull request, run your changes through Claude:

"Review the changes in this branch for bugs, security issues, and code quality problems. Be thorough and critical."

Step 2: Targeted Reviews

For specific concerns, ask targeted questions:

"Check this code for SQL injection vulnerabilities"

"Are there any race conditions in this async code?"

"Will this function handle empty input correctly?"

"Is this consistent with our error handling patterns?"

Step 3: Fix and Verify

When Claude finds issues, ask it to fix them and explain the fix:

"Fix the SQL injection vulnerability you found and explain what made the original code unsafe"

Review Prompts That Work

Security-Focused

"Review this code as a security auditor. Look for OWASP top 10 vulnerabilities, authentication bypasses, and data exposure risks."

Performance-Focused

"Analyze this code for performance issues. Look for N+1 queries, memory leaks, unnecessary computations, and scalability concerns."

Maintainability-Focused

"Review this code for maintainability. Look for unclear naming, missing documentation, complex logic that should be simplified, and violations of our coding standards."

Real Example: Security Issue Caught

// Original code looked fine to human reviewers

const user = await db.query(
  `SELECT * FROM users WHERE id = '${userId}'`
);

// Claude's response:

"This code is vulnerable to SQL injection. The userId is directly interpolated into the query string. If userId contains malicious SQL like 1' OR '1'='1, an attacker could access all user records."

// Fixed version:

const user = await db.query(
  'SELECT * FROM users WHERE id = $1',
  [userId]
);

Combining AI and Human Review

The best code review process uses both:

AI first pass: Catch mechanical issues, security problems, edge cases
Author revision: Fix AI-identified issues before human review
Human review: Focus on architecture, design decisions, business logic
AI verification: Confirm fixes don't introduce new issues

This workflow respects everyone's time. Humans don't waste energy on issues AI can catch. AI handles the tedious scanning. The result is faster, more thorough reviews.