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Attoflow Risk Assessment

Attoflow Risk Assessment

attoflow

|
3 installs
| (0) | Free
AI-powered risk assessment for documents
Installation
Launch VS Code Quick Open (Ctrl+P), paste the following command, and press enter.
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Attoflow Risk Assessment VS Code Extension

A VS Code extension that provides real-time code risk assessment for Python, JavaScript, and TypeScript files using machine learning models.

Features

  • Real-time code analysis
  • Support for multiple languages:
    • Python
    • JavaScript
    • TypeScript
  • Risk level assessment (Low, Medium, High)
  • Detailed recommendations for code improvement
  • Visual indicators in the status bar
  • Inline diagnostics for high-risk code

Requirements

  • Python 3.8 or higher
  • VS Code 1.60.0 or higher
  • Required Python packages (installed automatically):
    • numpy
    • scikit-learn
    • joblib
    • pandas
    • xgboost
    • imbalanced-learn
    • esprima (for JavaScript/TypeScript parsing)

Installation

  1. Install the extension from the VS Code marketplace
  2. Install Python dependencies:
    pip install -r requirements.txt
    
  3. Restart VS Code

Usage

The extension automatically analyzes Python, JavaScript, and TypeScript files as you type. You can also manually trigger analysis:

  1. Open a supported file
  2. Press Ctrl+Shift+P (Windows/Linux) or Cmd+Shift+P (Mac)
  3. Type "Analyze Code Risk" and select the command

Status Bar Indicators

  • 🟢 Low Risk
  • 🟡 Medium Risk
  • 🔴 High Risk

Configuration

The extension can be configured in VS Code settings:

  • attoflowRiskAssessment.enable: Enable/disable real-time analysis
  • attoflowRiskAssessment.updateInterval: Analysis update interval in milliseconds
  • attoflowRiskAssessment.languages: List of languages to analyze (default: ["python", "javascript", "typescript"])

How It Works

  1. The extension extracts features from your code:

    • Code complexity
    • Lines of code
    • Comment ratio
    • Nesting depth
    • Variable count
    • Function count
    • Class count
    • Import count
    • Exception count
    • Loop count
  2. These features are analyzed by multiple ML models:

    • Random Forest
    • SVM
    • XGBoost
  3. The models' predictions are combined to provide:

    • Overall risk level
    • Probability of defects
    • Language-specific recommendations

Language-Specific Features

Python

  • AST-based analysis
  • Python-specific complexity metrics
  • PEP 8 style recommendations

JavaScript/TypeScript

  • Esprima-based parsing
  • Modern JS/TS features support
  • Recommendations for:
    • Object destructuring
    • Array methods
    • Higher-order functions
    • Async/await patterns

Development

Building from Source

  1. Clone the repository
  2. Install dependencies:
    npm install
    pip install -r requirements.txt
    
  3. Build the extension:
    npm run compile
    

Running Tests

python -m unittest discover tests

Contributing

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

License

MIT

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