Code Quality Checker

Analyze code quality with AI-powered assessment that evaluates readability, maintainability, security, performance, and best practices adherence. Automatically calculates cyclomatic complexity, identifies code smells, detects security vulnerabilities, and measures technical debt. Provides detailed quality scores across multiple dimensions including naming conventions, error handling, documentation completeness, and SOLID principle violations across Python, JavaScript, Java, C++, and 100+ languages instantly.

Code Quality Analyzer
Tools
INPUT
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OUTPUT
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Hint: Paste your code and click Quality to analyze code quality, identify issues, and get improvement suggestions.

We never store your code

How It Works

  1. Step 1: Paste your code for quality analysis—the AI examines function length, variable naming, complexity metrics, and architectural patterns.
  2. Step 2: The AI performs multi-dimensional analysis calculating cyclomatic complexity (decision points), cognitive complexity (human understanding difficulty), code duplication percentage, and adherence to language-specific style guides (PEP 8 for Python, ESLint for JavaScript).
  3. Step 3: Advanced static analysis detects code smells (Long Method, Large Class, Feature Envy), security vulnerabilities (SQL injection risks, XSS vulnerabilities, hardcoded credentials), and performance anti-patterns (N+1 queries, unnecessary loops).
  4. Step 4: Receive a comprehensive quality report with scores (0-10) for readability, maintainability, security, performance, and documentation, plus actionable recommendations with specific line numbers and refactoring suggestions.

Quality Metrics Comparison

Feature Low Quality Code High Quality Code
Readability Score Poor naming, complex logic Clear, self-documenting code
Maintainability High complexity (>10) Low complexity (<5)
Security Vulnerabilities present Security best practices
Performance Inefficient patterns Optimized algorithms
Documentation Missing or outdated Comprehensive comments

Code Quality Analysis Examples

Example 1: Readability and Maintainability Issues

Low Quality Python Input
def p(d, t):
    r = []
    for i in d:
        if i['t'] == t:
            r.append(i)
    return r

def c(d):
    t = 0
    for i in d:
        t += i['p']
    return t
Quality Report Output
QUALITY REPORT
==============

Overall Score: 3.2/10 (Poor)

READABILITY: 2/10
- Issue: Single-letter variable names (d, t, r, i, p)
  Line 1: Function name 'p' is not descriptive
  Line 2: Variable 'r' should be 'results' or 'filtered_items'
  Recommendation: Use descriptive names like 'filter_by_type' instead of 'p'

- Issue: No docstrings or comments
  Line 1-6: Missing function documentation
  Recommendation: Add docstring explaining parameters and return value

MAINTAINABILITY: 3/10
- Cyclomatic Complexity: 2 (Acceptable)
- Code Duplication: 40% (High)
  Lines 1-6 and 8-12 have similar iteration patterns
  Recommendation: Extract common iteration logic

BEST PRACTICES: 4/10
- Issue: Not following PEP 8 naming conventions
  Functions should have descriptive snake_case names
  
- Issue: No type hints (Python 3.5+)
  Line 1: Add type hints for parameters and return value
  Recommendation: def filter_by_type(data: List[Dict], type_filter: str) -> List[Dict]:

PERFORMANCE: 6/10
- Issue: Multiple list iterations could be combined
  Recommendation: Use list comprehension for better performance

DOCUMENTATION: 1/10
- Missing: Function docstrings
- Missing: Parameter descriptions
- Missing: Return value documentation

SUGGESTED IMPROVEMENTS:
1. Rename 'p' to 'filter_items_by_type'
2. Rename 'c' to 'calculate_total_price'
3. Add type hints and docstrings
4. Use list comprehensions
5. Extract common patterns into helper functions

Key Changes:

The quality report demonstrates comprehensive analysis across five dimensions with specific, actionable feedback. The readability score is low (2/10) due to cryptic single-letter names violating the self-documenting code principle—'p', 'd', 't' provide zero semantic information. The analyzer identifies each problematic variable with line numbers and suggests descriptive alternatives like 'filter_by_type' and 'filtered_items'. Maintainability analysis calculates cyclomatic complexity (2, acceptable) but flags 40% code duplication from similar iteration patterns, recommending extraction to a common function. Best practices evaluation checks PEP 8 compliance and modern Python features, noting missing type hints available since Python 3.5. Performance analysis identifies optimization opportunities like list comprehensions replacing manual loops. Documentation score is critically low (1/10) due to missing docstrings, violating Python's documentation culture. The report provides prioritized recommendations with specific code examples, making improvements actionable. This pattern helps developers understand not just what is wrong, but why it matters and how to fix it, supporting continuous quality improvement.

Example 2: Security and Performance Issues

Vulnerable JavaScript Input
const express = require('express');
const app = express();

app.get('/user', (req, res) => {
    const userId = req.query.id;
    const query = "SELECT * FROM users WHERE id = " + userId;
    
    db.query(query, (err, results) => {
        if (err) {
            res.send(err.message);
        }
        res.json(results);
    });
});

const API_KEY = "sk_live_abc123xyz";

app.listen(3000);
Quality Report Output
QUALITY REPORT
==============

Overall Score: 2.8/10 (Critical Issues Found)

SECURITY: 1/10 (CRITICAL)
⚠️ CRITICAL: SQL Injection Vulnerability
  Line 6: Direct string concatenation with user input
  Risk: Attacker can execute arbitrary SQL commands
  Example Attack: ?id=1 OR 1=1 -- 
  FIX: Use parameterized queries
  
  Secure Alternative:
  const query = "SELECT * FROM users WHERE id = ?";
  db.query(query, [userId], callback);

⚠️ CRITICAL: Hardcoded API Key
  Line 15: API key exposed in source code
  Risk: Key will be committed to version control
  FIX: Use environment variables
  
  Secure Alternative:
  const API_KEY = process.env.API_KEY;

⚠️ HIGH: Error Message Exposure
  Line 10: Sending raw error messages to client
  Risk: Leaks internal system information
  FIX: Log errors server-side, send generic message to client

PERFORMANCE: 4/10
- Issue: No connection pooling visible
  Recommendation: Use connection pool for database queries
  
- Issue: No caching strategy
  Recommendation: Implement Redis caching for frequently accessed users

READABILITY: 5/10
- Issue: No input validation
  Line 5: userId not validated before use
  Recommendation: Validate and sanitize all user inputs

MAINTAINABILITY: 4/10
- Issue: No error handling for database connection
- Issue: Missing async/await pattern (callback hell risk)
  Recommendation: Use async/await with try-catch

BEST PRACTICES: 3/10
- Missing: Request rate limiting
- Missing: CORS configuration
- Missing: Helmet.js security headers
- Missing: Input validation middleware

CRITICAL ACTIONS REQUIRED:
1. FIX SQL INJECTION: Use parameterized queries immediately
2. REMOVE HARDCODED SECRETS: Move to environment variables
3. IMPLEMENT INPUT VALIDATION: Validate userId is numeric
4. ADD ERROR HANDLING: Don't expose internal errors
5. ADD SECURITY MIDDLEWARE: Helmet, rate limiting, CORS

Key Changes:

This quality report prioritizes critical security vulnerabilities that could lead to data breaches, demonstrating the tool's ability to identify production-blocking issues. The SQL injection vulnerability (Security: 1/10) is flagged as CRITICAL with a concrete attack example showing how ?id=1 OR 1=1 -- bypasses authentication. The report provides the exact fix using parameterized queries with placeholders, preventing SQL injection through proper escaping. The hardcoded API key issue is explained with context—version control exposure—and the environment variable solution. Error message exposure is rated HIGH risk because stack traces leak database schema and system architecture to attackers. Performance analysis identifies missing connection pooling, which causes database connection exhaustion under load, and lack of caching for read-heavy endpoints. Best practices evaluation checks for Express.js security middleware—Helmet for HTTP headers, rate limiting for DDoS protection, CORS for cross-origin security. The report uses visual indicators (⚠️ CRITICAL) to draw attention to severe issues and provides code examples for every recommendation, not just descriptions. The prioritized action list helps developers triage fixes—security vulnerabilities before performance optimizations. This pattern is essential for security-conscious development, preventing common OWASP Top 10 vulnerabilities from reaching production.

Frequently Asked Questions

What is a code quality checker?

A code quality checker is an AI-powered tool that analyzes your code to evaluate its quality across multiple dimensions including readability, maintainability, performance, security, and adherence to best practices. It provides detailed reports with quality scores and improvement recommendations.

What does the code quality analyzer check?

The analyzer evaluates: Readability (code clarity and naming), Maintainability (code structure and complexity), Error Handling (exception and edge cases), Best Practices (coding standards), Security (vulnerabilities and risks), Performance (efficiency and optimization), Documentation (comments and docs), and Code Smells (anti-patterns).

How is the code quality score calculated?

The quality score is calculated based on multiple factors: readability (20%), maintainability (20%), error handling (15%), best practices (15%), security (15%), performance (10%), and documentation (5%). Each factor is analyzed and scored out of 10, then combined for an overall quality score.

Is the code quality checker free?

Yes! Every new user gets 5 free lifetime credits. After that, purchase credits starting at $10 for 100 credits. Credits never expire and there are no subscriptions.

Which programming languages are supported?

We support 100+ programming languages including Python, JavaScript, TypeScript, Java, C++, C#, PHP, Ruby, Go, Rust, Swift, Kotlin, and more. The analyzer applies language-specific quality standards and best practices.

What's the difference between code quality and code review?

Code quality analysis focuses on automated metrics and objective measurements like complexity, maintainability, and adherence to standards. Code review includes subjective human feedback on design decisions, architecture, and context-specific improvements. Our tool provides both automated quality analysis and AI-powered review suggestions.