DoraCodeLens VS Code Extension

Intro

Every developer knows the pain of diving into an unfamiliar codebase — endless scrolling, manual tracing, and juggling scattered tools. We wanted to replace that friction with clarity: a single workspace inside VS Code supported IDE where a Python project comes alive as a visual mind map.

That’s how DoraCodeLens was born.

Features

• Mind maps of modules, classes, methods, and functions — annotated with real-time complexity scores of any file or full code and inline complexities displayed on top of functions & classes.
• Database-schema diagrams, extracted directly from ORM models.
• Tech Stack and Framework Detection: Automatic detection of Django, Flask, FastAPI etc patterns
• Full Code Analysis: Complete codebase analysis with interactive visualizations
• Current File Analysis: Quick insights for the file you're working on
• Inline Code Lens: Automatic complexity indicators (🟢🟡🔴) that appear after current file analysis
• **Git Analytics **: Contributor statistics and commit timeline visualizations
• Productivity tools such as JSON minify/beautify and a tree viewer.

Gallery

Mind Map Visualization

File Inline complexity Analysis

Database Schema Visualization

Tech Stack Detection

Git Analytics

Quick Start

1. Install the Extension

• Open VS Code Extensions panel (Ctrl+Shift+X)
• Search for "DoraCodeLens"
• Click Install and reload VS Code

2. Analyze Your Project

• Open a Python project in VS Code
• Right-click on any Python file
• Select DoraCodeLens from the context menu and choose any option

Troubleshooting

Common Issues

Extension not activating

• Ensure you have a Python project open with .py files
• Check that Python 3.8+ is installed and accessible
• Verify workspace contains requirements.txt, pyproject.toml, or Python files
• Try reloading VS Code window (Ctrl+Shift+P → "Developer: Reload Window")

Analysis fails or times out

• Check Python interpreter path in DoraCodeLens settings
• Increase analysis timeout in settings (default: 120 seconds)
• Try Current File Analysis instead of Full Code Analysis for large projects
• Review VS Code Output panel for detailed error messages

Inline complexity indicators not appearing

• Run "Current File Analysis" first - indicators auto-enable after analysis
• Check that you're in a Python file (.py extension)
• Ensure the file contains functions with complexity data
• Verify complexity thresholds in settings aren't too restrictive
• Toggle Code Lens manually if needed (DoraCodeLens: Enable Code Lens Inline)

Git Analytics empty

• Ensure the project is a Git repository (git init if needed)
• Check that Git is installed and accessible from command line
• Verify the repository has commit history
• Try analyzing a specific date range if history is very large

Database Schema not detected

• Ensure you have Django models (models.py) or SQLAlchemy models
• Check that model files are in standard locations
• Verify database models follow standard ORM patterns
• Look for SQL files in migrations or schema directories

JSON utilities not working

• Ensure the content is valid JSON syntax
• Try formatting smaller JSON sections first
• Check that the file has proper JSON structure
• Use JSON Fix feature for Python dictionary conversion

Performance issues with large projects

• Use Current File Analysis instead of Full Code Analysis
• Enable analysis caching in settings
• Close other resource-intensive VS Code extensions
• Consider analyzing specific directories rather than entire project
• Ensure sufficient RAM (4GB+ recommended for large projects)

Getting Help

1. Check Extension Output

• Open VS Code Output panel (View → Output)
• Select "DoraCodeLens" from the dropdown
• Review error messages and debug information

2. Enable Debug Logging

• Open VS Code Settings (Ctrl+,)
• Search for "DoraCodeLens debug"
• Enable "Enable Debug Logging"
• Reproduce the issue and check output

System Requirements

• VS Code or VS code based IDE: Version 1.74.0 or higher
• Python: Version 3.8 or higher
• Git: Any recent version (for Git analytics features)
• Memory: At least 4GB RAM recommended for large projects
• Storage: 100MB free space for extension and caching
• Network: Internet connection for initial Python package installation

License

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