VizCode

Table of Contents

• Project Overview
• Features
• Use Cases
• System Architecture
• Key Functionalities
• Screenshots/Visuals
• Collaborators
• Future Enhancements

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Project Overview

Understanding complex data structures is a significant challenge in computer science education. To address this, we developed the Interactive Data Structure Visualizer, a web-based platform that simplifies learning through real-time, Python-centric visualizations.

This innovative tool bridges the gap between theoretical concepts and practical understanding by offering dynamic visual aids, step-by-step execution tracing, and secure, containerized code execution. It’s designed for students, educators, and self-learners, fostering intuitive and hands-on exploration of data structures.

Features

• Real-Time Visualization
  Observe live transformations in data structures during code execution.
• Interactive Debugging
  Step through operations like insertion, deletion, and searching to track variable changes.
• Browser-Based and Platform-Independent
  Accessible on any device without installations.
• Secure Code Execution
  Sandboxed environments ensure security for multi-user functionality.
• Educational Enhancements
  Includes intuitive error handling and conceptual explanations for each data structure.

Use Cases

1. For Students
   Visualize operations on data structures to grasp concepts effectively.
2. For Educators
   Demonstrate real-time transformations in classroom environments.
3. For Developers
   Debug and analyze code behavior intuitively.

System Architecture

The application follows a modular architecture:

• Frontend: Interactive UI with visualization components.
• Code Preprocessor: Manages code parsing and preparation for execution.
• Code Executor: Handles code execution requests and returns results after safe execution within isolated environments.
• Visualization Engine: Renders real-time visualizations based on execution data.

Key Functionalities

• Code Input and Execution
  Submit Python code snippets and observe their execution.
• Visualization
  Displays step-by-step transformations in data structures like arrays, trees, and graphs.
• Error Handling
  Detects and highlights errors with detailed feedback.
• Integration
  Easily embeddable in Learning Management Systems (LMS) or educational platforms.

Screenshots/Visuals

(Coming Soon)

Collaborators

• Jayanth R
• Mekha L
• Srilakshmi R
• Vignesh S Naik

Future Enhancements

• Support for additional programming languages like Java and C++.
• Integration with AI-driven feedback to suggest optimizations.
• Advanced visualizations for algorithms like dynamic programming and graph traversals.

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Links: Front-End repo