🎯 AI Code Quality Predictor

AI-Powered Code Quality Analysis and Bug Prediction

Never ship buggy code again! This VS Code extension uses machine learning to predict code quality issues, detect potential bugs, and provide intelligent recommendations for improving your codebase.

🎯 Core Features

🔮 Smart Quality Analysis

• Real-time Quality Scoring: Analyzes code patterns and assigns quality scores (0-100)
• Bug Risk Assessment: Predicts the likelihood of bugs in your code
• Security Vulnerability Detection: Identifies potential security issues
• Performance Issue Prediction: Spots code that might impact performance

📊 Advanced Analytics

• Complexity Analysis: Tracks cyclomatic and cognitive complexity
• Code Pattern Recognition: Identifies problematic patterns and anti-patterns
• Technical Debt Tracking: Monitors accumulation of technical debt
• Maintainability Scoring: Predicts how difficult code will be to maintain

🚨 Intelligent Alerts

• Predictive Warnings: Get notified before quality issues become problems
• Priority-based Issues: High, medium, and low priority issue classification
• Smart Thresholds: Dynamic alerts based on your coding patterns
• Real-time Feedback: Instant quality feedback as you code

📈 Local-Only Operation

• No API Required: Works entirely offline with local analysis
• Privacy First: All analysis stays on your machine
• Fast Performance: Instant analysis without network calls
• Secure: No code sent to external servers

🚀 Quick Start

Installation

For VS Code:

# Install from VSIX
code --install-extension code-quality-predictor-2.0.0.vsix

For Windsurf IDE:

# Uninstall previous version if it exists
windsurf --uninstall-extension FinsavvyAI.code-quality-predictor

# Install from VSIX
windsurf --install-extension code-quality-predictor-2.0.0.vsix

Note: If you get a "command already exists" error, restart your IDE after uninstalling the previous version.

Initial Setup

1. Open a File: Open any code file to begin analysis
2. Enable Auto-Analysis: Extension automatically analyzes your code
3. View Quality Score: Check the status bar for your quality score

📋 Commands

🧪 Analysis Models

Quality Scoring

// The extension analyzes multiple factors:
- Code complexity (cyclomatic & cognitive)
- Function length and structure
- Code duplication patterns
- Security vulnerability patterns
- Performance anti-patterns
- Maintainability indicators

AI-Based Predictions

• Bug Risk Prediction: Machine learning models predict bug likelihood
• Complexity Analysis: Identifies overly complex code structures
• Pattern Recognition: Detects code smells and anti-patterns
• Security Analysis: Identifies potential security vulnerabilities

📊 Status Bar Indicators

[🟢 85/100 Quality] [🟢 15% Risk]
     ↑                    ↑
Quality Score        Bug Risk Level

Color Coding

• 🟢 Green: High quality (75+ score), Low risk (<30%)
• 🟡 Yellow: Medium quality (50-74 score), Medium risk (30-60%)
• 🔴 Red: Poor quality (<50 score), High risk (>60%)

⚙️ Configuration

{
  "codeQuality.qualityThreshold": 75,
  "codeQuality.notifyOnLowQuality": true,
  "codeQuality.bugRiskThreshold": 30,
  "codeQuality.autoAnalyzeOnSave": true,
  "codeQuality.analysisDepth": "thorough",
  "codeQuality.enableSecurityAnalysis": true,
  "codeQuality.enablePerformanceAnalysis": true,
  "codeQuality.debugMode": false
}

🎛️ Advanced Features

Project Analysis

The extension automatically analyzes your project to assess overall quality:

• Codebase Health: Overall project quality assessment
• Hotspot Detection: Identifies files most likely to have bugs
• Dependency Analysis: Analyzes code coupling and dependencies
• Test Coverage Suggestions: Recommends areas needing more tests

Smart Issue Detection

Automatically detects various types of issues:

// Patterns that trigger quality warnings:
- High cyclomatic complexity (>10)
- Long functions (>50 lines)
- Deep nesting levels (>4)
- Code duplication
- Security vulnerabilities
- Performance bottlenecks

Quality Improvement Tips

AI-powered suggestions to improve code quality:

• Refactoring Recommendations: Specific suggestions for code improvements
• Pattern Improvements: Better ways to implement common patterns
• Security Hardening: Security best practices
• Performance Optimization: Performance improvement suggestions

📈 Analytics Dashboard

Access detailed analytics through the sidebar panel:

Quality Tab

• Current file quality score
• Bug risk assessment
• Issue breakdown by category
• Improvement recommendations

Predictions Tab

• AI-generated quality forecasts
• Bug likelihood predictions
• Maintenance difficulty estimates
• Security risk assessments

History Tab

• Quality trends over time
• Historical analysis data
• Pattern evolution
• Improvement tracking

🔧 Development & Testing

Local Development

# Clone the repository
git clone https://github.com/FinsavvyAI/code-quality-predictor

# Install dependencies
npm install

# Compile TypeScript
npm run compile

# Run in development mode
npm run watch

# Run tests
npm test

# Package extension
npm run package

Testing the Analysis

# Generate test data for ML model training
Command Palette → "Code Quality: Generate Test Data"

# Enable debug mode for detailed logging
Settings → codeQuality.debugMode: true

# View analysis accuracy metrics
Command Palette → "Code Quality: Show Debug Info"

🤝 Contributing

We welcome contributions! Here's how to get started:

1. Fork the Repository: Create your own fork of the project
2. Create a Feature Branch: git checkout -b feature/analysis-improvements
3. Make Changes: Add new analysis models or features
4. Test Thoroughly: Ensure all existing functionality works
5. Submit PR: Create a pull request with detailed description

Areas for Contribution

• New Analysis Models: Improve accuracy with better algorithms
• Language Support: Add support for more programming languages
• Performance Optimization: Make analysis faster
• UI/UX Improvements: Enhance the user interface

📚 Technical Architecture

Core Components

src/
├── prediction/          # AI analysis engine
│   ├── models/         # Quality prediction models
│   ├── training/       # Model training logic
│   └── inference/      # Real-time analysis
├── tracking/           # Quality tracking system
├── ui/                 # VS Code UI components
├── storage/            # Local data persistence
└── analytics/          # Analysis and reporting

Data Flow

1. Code Analysis: Extension monitors code changes
2. Quality Assessment: AI models analyze code patterns
3. Prediction Generation: Real-time quality predictions
4. Issue Detection: Identifies potential problems
5. User Alerts: Intelligent notifications and recommendations

🛡️ Privacy & Security

• Local-Only Processing: All analysis remains on your machine
• No External APIs: No code sent to servers
• Encrypted Storage: Local data is encrypted at rest
• Open Source: Full transparency of analysis methods

📄 License

MIT License - see LICENSE file for details.

🆘 Support

• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Email: support@finsavvy.ai

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