🔍 Code Quality & Security Audit System

An AI-powered code quality and security audit system that combines pattern-based analysis with local AI models to identify vulnerabilities, code quality issues, and provide actionable recommendations.

🌐 Try It Live!

🌍 Real-World Impact:

• 🔒 Helps teams build secure, compliant applications faster
• 📉 Reduces time spent on manual code review by 50–70%
• 📊 Improves engineering efficiency and software audit readiness
• 🧩 Easy to integrate into existing DevOps and CI/CD pipelines

✨ Features

• Security Analysis: Detects vulnerabilities like SQL injection, XSS, hardcoded secrets, and more using regex patterns and AI analysis.
• Code Quality Checks: Identifies issues like code duplication, complex functions, and missing docstrings.
• AI Integration: Leverages local Ollama models (deepseek-coder:6.7b, deepseek-r1:1.5b, deepscaler) for advanced analysis.
• Database Storage: Saves audit reports and issues in a SQLite database for historical tracking.
• Interactive Dashboard: Visualizes results using Streamlit and Plotly for easy interpretation.
• Compliance Reporting: Supports standards like PCI-DSS, SOX, GDPR, HIPAA, and ISO 27001 (placeholder implementation).

• CLI and GUI Support: Run audits via command-line interface or interactive web dashboard.

• 🔒 Security Analysis: OWASP Top 10 vulnerability detection

• 📊 Code Quality: Maintainability and complexity analysis

• 🤖 AI-Powered: Local Ollama integration for advanced insights

• 🌐 Web Dashboard: Interactive Streamlit interface

• 💻 CLI Tool: Comprehensive command-line interface

• 📈 Historical Tracking: Trend analysis and reporting

• 🐳 Docker Ready: Containerized deployment

• 🔧 Configurable: Customizable patterns and thresholds

🚀 Quick Start

Option 1: One-Command Installation

Linux/macOS: bash curl -fsSL https://raw.githubusercontent.com/muhammadfarhantanvir/Code-Quality-Security-Audit-System-AI-Agent/main/scripts/install.sh | bash

Windows: powershell iwr -useb https://raw.githubusercontent.com/muhammadfarhantanvir/Code-Quality-Security-Audit-System-AI-Agent/main/scripts/install.bat | iex

Option 2: Docker (Recommended)

bash git clone https://github.com/muhammadfarhantanvir/Code-Quality-Security-Audit-System-AI-Agent cd Code-Quality-Security-Audit-System-AI-Agent docker-compose -f docker/docker-compose.yml up -d

Open http://localhost:8501 in your browser.

Option 3: Manual Installation

```bash

Clone repository

git clone https://github.com/muhammadfarhantanvir/Code-Quality-Security-Audit-System-AI-Agent cd Code-Quality-Security-Audit-System-AI-Agent

Install dependencies

pip install -r requirements.txt

Run CLI audit

python main.py --directory /path/to/your/project

Launch dashboard

streamlit run dashboard.py ```

💻 Usage

Command Line Interface

```bash

Basic audit

python main.py --directory ./my-project

Generate reports

python main.py --directory ./my-project --output report.json --export-html

Filter by severity

python main.py --directory ./my-project --severity HIGH --verbose

Disable AI for faster scanning

python main.py --directory ./my-project --no-ai ```

Web Dashboard

bash streamlit run dashboard.py

Then open http://localhost:8501 and enter your project path.

Using Make Commands

```bash

Setup everything

make setup

Run audit on current directory

make audit

Launch dashboard

make dashboard

Run tests

make test

See all commands

make help ```

📁 Project Structure

Code-Quality-Security-Audit-System-AI-Agent/ ├── src/code_audit_system/ # Main package │ ├── core/ # Core functionality │ │ ├── auditor.py # Main auditor class │ │ ├── models.py # Data models │ │ └── patterns.py # Security/quality patterns │ ├── ai/ # AI integration │ │ └── ollama_client.py # Ollama client │ ├── dashboard/ # Web interface │ │ └── streamlit_app.py # Streamlit dashboard │ └── cli/ # Command-line interface │ └── main.py # CLI implementation ├── config/ # Configuration files ├── docs/ # Documentation ├── scripts/ # Installation scripts ├── docker/ # Docker configuration ├── tests/ # Test suite ├── assets/ # Images and examples ├── main.py # CLI entry point ├── dashboard.py # Dashboard entry point └── Makefile # Project management

🔍 What It Detects

Security Issues

• SQL Injection vulnerabilities
• Cross-Site Scripting (XSS)
• Hardcoded secrets and passwords
• Command injection flaws
• Insecure communication (HTTP)
• Path traversal vulnerabilities
• Weak cryptography usage
• And more OWASP Top 10 issues...

Code Quality Issues

• Long and complex functions
• Code duplication
• Missing documentation
• Poor error handling
• Magic numbers
• Deep nesting
• And more maintainability issues...

🤖 AI Integration

The system integrates with local Ollama models for advanced analysis:

• DeepSeek Coder: Code security analysis
• DeepSeek R1: Strategic recommendations
• DeepScaler: Technical debt prediction

Install Ollama and pull models: ```bash

Install Ollama

curl -fsSL https://ollama.ai/install.sh | sh

Pull models

ollama pull deepseek-coder:6.7b ollama pull deepseek-r1:1.5b ollama pull deepscaler ```

📊 Supported Languages

• Python (.py)
• JavaScript/TypeScript (.js, .jsx, .ts, .tsx)
• Java (.java)
• C/C++ (.c, .cpp)
• PHP (.php)
• Ruby (.rb)
• Go (.go)
• Rust (.rs)
• C# (.cs)
• Swift (.swift)

🔧 Configuration

Customize the system via config/config.yaml:

```yaml

Scanning options

scanning: maxfilesize: 2000 excludepatterns: - "*/nodemodules/" - "/venv/*"

AI settings

ollama: base_url: "http://localhost:11434" timeout: 60

Security patterns (customizable)

security: severity_weights: CRITICAL: 15 HIGH: 10 MEDIUM: 5 ```

📈 Example Output

```bash

🔍 Code Quality & Security Audit System v1.0.0

📁 Scanning directory: ./my-project

✅ Audit completed in 3.45 seconds! 📊 Files scanned: 25 📏 Total lines: 2,847 🔐 Security issues: 3 📊 Quality issues: 12 ⚠️ Risk score: 45.2/100 🟡 MEDIUM RISK - Review and address issues

🔴 Top Security Issues: 1. HIGH - SQL Injection in database.py:42 2. MEDIUM - Hardcoded Secret in config.py:15 3. MEDIUM - XSS Vulnerability in views.py:128

💡 Recommendations: 1. URGENT: Address 1 high-severity security vulnerabilities immediately 2. Implement security code review process 3. Refactor code to improve maintainability ```

🧪 Testing

```bash

Run all tests

make test

Run with coverage

pytest --cov=src --cov-report=html

Run specific tests

pytest tests/testsecuritypatterns.py -v ```

🤝 Contributing

We welcome contributions! See docs/CONTRIBUTING.md for guidelines.

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests
5. Submit a pull request

📚 Documentation

• Getting Started Guide - Comprehensive setup guide
• Contributing Guidelines - How to contribute
• Changelog - Version history
• Configuration Reference - All configuration options

🐳 Docker Deployment

```bash

Build and run

docker-compose -f docker/docker-compose.yml up -d

Scale services

docker-compose -f docker/docker-compose.yml up -d --scale code-audit-system=3

View logs

docker-compose -f docker/docker-compose.yml logs -f ```

🔒 Security

This tool runs entirely locally - no data is sent to external services. AI analysis uses local Ollama models for privacy and security.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• OWASP for security vulnerability classifications
• The Ollama team for local AI model infrastructure
• The open-source security community

📞 Support

• 🐛 Bug Reports: GitHub Issues
• 💡 Feature Requests: GitHub Discussions
• 📖 Documentation: Getting Started Guide

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