CodeOracle AI Analysis

CodeOracle AI is a Visual Studio Code extension that delivers AI-powered insights into your source code. The tool performs detailed static analysis—extracting Abstract Syntax Trees (AST), Control Flow Graphs (CFG), and Data Flow Graphs (DFG)—and leverages GPT-4 to produce clear, tutor-style feedback. By integrating with a Flask-based backend, it provides both a comprehensive textual report and intuitive flow diagrams, allowing you to quickly identify potential issues, optimisations, and best practices.

Key Features

• Prompt, Tutor-Style Feedback
  Provides clear, actionable insights into your code’s structure and potential issues.

• On-Demand Analysis
  Analyse the currently open file with a single click for an extensive report.

• Visual Diagrams
  Automatically generates CFG diagrams using Mermaid for quick comprehension.

• Multi-Language Support
  Uses Tree-sitter to parse Python, JavaScript, and TypeScript.

• AI-Driven Summaries
  Employs GPT-4 to refine outputs and deliver concise, educational commentary.

• Secure & Efficient Caching
  Utilises MongoDB with TTL to automatically delete analysis data and avoid storing source code.

• Asynchronous Processing
  A Flask backend handles requests in real-time, supporting multiple analyses concurrently.

Requirements

• Visual Studio Code
  Version 1.97.0 or later.

• Backend Analysis Server
  A Flask-based server exposing an /analyse endpoint.
  (Repository link will be updated soon; see below for temporary usage instructions.)

• Node.js & npm
  Required for building and packaging this extension if installing from source.

Installation

1. Installing via the VS Code Marketplace

1. Open VS Code.
2. Go to the Extensions panel.
3. Search for CodeOracle AI.
4. Click Install.

2. Setup & Usage of the Backend

Note: The official backend repository link will be updated soon to encourage community collaboration. Meanwhile, you can follow these steps:

1. Clone or Download the Flask Backend

   git clone <temporary-backend-repo-url>
   cd flask-analysis-backend
   
   

2. Install Python Dependencies

   pip install -r requirements.txt
   
   

3. Configure Environment Variables MONGODB_URI for connecting to your MongoDB instance OPENAI_API_KEY for AI-driven summarisation

4. Run the Flask Server

   python app.py
   

Ensure the server is accessible at http://localhost:5000/analyse for the extension to function along with the backend analysis server.

5. Connect with the VS Code Extension Once the server is running, open any source file in VS Code, trigger the "Analyse Current File" command, and the extension will send your code to the /analyse endpoint for processing.

6. Caching & Privacy The backend uses MongoDB caching with TTL to ensure analysis data is automatically deleted over time without storing source code content.

This setup allows the extension to leverage both static analysis (AST, CFG, DFG) and AI-driven insights. Detailed reports and visual diagrams will appear in the extension’s panel once the analysis completes.