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.
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Issue Description
Hint: Paste your code and click Quality to analyze code quality, identify issues, and get improvement suggestions.
How It Works
- Step 1: Paste your code for quality analysis—the AI examines function length, variable naming, complexity metrics, and architectural patterns.
- 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).
- 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).
- 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| Documentation | Missing or outdated | Comprehensive comments |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
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
==============
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
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
==============
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.