AI Code Review Tool (Free)

AI-powered code review for Python, JavaScript, Java, C++, and 100+ languages. Detect security vulnerabilities, performance issues, code smells, and best practice violations. Get instant feedback with improvement suggestions and line-by-line analysis.

Code Reviewer
Tools
INPUT
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OUTPUT
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Hint: Paste your code and click Review to get a detailed code review with security, performance, and quality insights.

We never store your code

How It Works

  1. 1

    Submit Your Code for Review

    Paste functions, classes, modules, or entire files from any programming language. Supports Python, JavaScript, TypeScript, Java, C++, C#, PHP, Go, Rust, Ruby, and 100+ languages.

  2. 2

    AI Analyzes Code Quality

    Advanced AI scans for security vulnerabilities (OWASP Top 10), performance bottlenecks, code smells, complexity issues, error handling gaps, naming violations, and violations of SOLID principles and language-specific best practices.

  3. 3

    Receive Detailed Feedback

    Get comprehensive review with specific line numbers, severity ratings (Critical/High/Medium/Low), issue descriptions, remediation guidance, code examples, and actionable improvement suggestions.

Manual vs AI Code Review

Feature Manual Review AI Review
Speed Hours per review Seconds per review
Consistency Varies by reviewer Always consistent
Coverage May miss issues Comprehensive checks
Availability Depends on team 24/7 instant
Focus Mixed (style + logic) Catches common issues
Best Use Architecture decisions Bug/security detection

Examples

Example 1: Security Vulnerability Detection

Python (Vulnerable) Input
import sqlite3

def get_user(username):
    conn = sqlite3.connect("users.db")
    cursor = conn.cursor()
    query = f"SELECT * FROM users WHERE username = '{username}'"
    cursor.execute(query)
    user = cursor.fetchone()
    return user
Python (Fixed) Output
import sqlite3
from typing import Optional

def get_user(username: str) -> Optional[tuple]:
    """
    Retrieve user from database securely using parameterized query.
    
    Args:
        username: Username to search (validated input)
    
    Returns:
        User tuple or None if not found
    """
    # Input validation
    if not username or not username.isalnum():
        raise ValueError("Invalid username format")
    
    try:
        with sqlite3.connect("users.db") as conn:
            cursor = conn.cursor()
            # Use parameterized query to prevent SQL injection
            query = "SELECT * FROM users WHERE username = ?"
            cursor.execute(query, (username,))
            return cursor.fetchone()
    except sqlite3.Error as e:
        logging.error(f"Database error: {e}")
        raise

Key Changes:

AI Review Findings: **CRITICAL SECURITY ISSUE - SQL Injection Vulnerability (CWE-89)** detected on line 6. Original code uses f-string concatenation to build SQL query, allowing attackers to inject malicious SQL. Example attack: username equals admin OR 1 equals 1 bypasses authentication. **Fixes Applied:** (1) Replaced f-string with parameterized query using ? placeholder, (2) Added input validation to check username format, (3) Implemented context manager (with statement) for proper resource cleanup, (4) Added type hints for better type safety, (5) Added comprehensive error handling with logging, (6) Added docstring documenting security considerations. **Impact:** Prevents SQL injection attacks, improves error handling, and follows secure coding practices. This vulnerability could lead to unauthorized data access, data manipulation, or complete database compromise.

Example 2: Performance Optimization Suggestions

JavaScript (Inefficient) Input
function processUsers(users) {
  let results = [];
  for (let i = 0; i < users.length; i++) {
    for (let j = 0; j < users.length; j++) {
      if (users[i].id === users[j].managerId) {
        results.push({
          employee: users[i].name,
          manager: users[j].name
        });
      }
    }
  }
  return results;
}
JavaScript (Optimized) Output
/**
 * Maps employees to their managers efficiently.
 * @param {Array<{id: number, name: string, managerId: number}>} users
 * @returns {Array<{employee: string, manager: string}>}
 */
function processUsers(users) {
  // Create O(1) lookup map: managerId -> manager object
  const managerMap = new Map(
    users.map(user => [user.id, user])
  );
  
  // Single pass through users array
  return users
    .filter(user => user.managerId && managerMap.has(user.managerId))
    .map(user => ({
      employee: user.name,
      manager: managerMap.get(user.managerId).name
    }));
}

Key Changes:

AI Review Findings: **PERFORMANCE ISSUE - Nested Loop O(n²) Complexity** detected. Original code has nested loops creating quadratic time complexity. For 1,000 users, this executes 1,000,000 iterations. **Inefficiencies Identified:** (1) Nested loops cause O(n²) time complexity, (2) No early exit optimization, (3) Array.push in loop is suboptimal, (4) Repeated array access inefficient. **Optimizations Applied:** (1) Created Map for O(1) manager lookup instead of O(n) linear search, (2) Reduced complexity from O(n²) to O(n), (3) Used functional programming (filter + map) for cleaner code, (4) Added JSDoc for documentation, (5) Added null/undefined checks for managerId. **Performance Impact:** For 10,000 users, execution time reduced from ~3 seconds to ~15ms (200x faster). Memory usage increased slightly for Map but worth the tradeoff. This optimization is crucial for real-time user interfaces processing large datasets.

Frequently Asked Questions

What does the AI code review check for?

The AI performs comprehensive analysis: security vulnerabilities (SQL injection, XSS, CSRF, buffer overflows), performance issues (inefficient algorithms, N+1 queries, memory leaks), code quality (cyclomatic complexity, duplicate code, long functions), best practices (SOLID principles, DRY, KISS), error handling (uncaught exceptions, missing validation), naming conventions (PEP 8, camelCase standards), maintainability issues (tight coupling, low cohesion), and testing gaps (missing edge cases, untested paths).

How accurate is AI code review compared to human review?

AI code review excels at catching syntax errors, security vulnerabilities, performance antipatterns, and style violations with near 100% consistency. It analyzes faster than humans and never misses documented rules. However, it cannot fully assess business logic correctness, architectural decisions, or context-specific requirements. Best practice: Use AI review first to catch 80% of common issues quickly, then human review for architectural insights, domain logic validation, and team-specific considerations.

Can it detect security vulnerabilities?

Yes. The AI detects OWASP Top 10 vulnerabilities: SQL injection, XSS, CSRF, insecure deserialization, broken authentication, sensitive data exposure, XML external entities, broken access control, security misconfiguration, and known vulnerable dependencies. It also identifies hardcoded credentials, weak cryptography, path traversal, command injection, and race conditions. Provides severity ratings (Critical, High, Medium, Low) and remediation guidance.

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