DataSmell

Find ML mistakes before they become expensive experiments.

DataSmell is the first developer-focused code smell detector built specifically for Machine Learning and Data Science code. It works locally and directly inside VS Code, analyzing Python files and Jupyter notebooks in real-time.

Why DataSmell?

Generic tools like SonarLint, ESLint, and Pylint don't understand Machine Learning workflows. DataSmell detects ML-specific bad practices like data leakage, missing random seeds, train/test pollution, and GPU memory leaks, helping you build robust models.

Features

• Real-Time Analysis: Detects issues as you type, highlighting them directly in the editor.
• Notebook Support: Analyzes Jupyter Notebook cells natively.
• Data Leakage Detection: Alerts when you fit a scaler before splitting your data.
• GPU Memory Checks: Catches missing torch.cuda.empty_cache() and missing model.eval().
• Beautiful Dashboard: Open the DataSmell Dashboard to see your Project Health Score and rule counts.
• Quick Fixes: One-click quick fixes for missing seeds, deprecated APIs, etc.
• 100% Local: No backend, no API keys, no data leaves your machine.

Supported Rules

Configuration

You can customize DataSmell in your VS Code settings:

• datasmell.enableNotebook: Analyze Jupyter Notebooks (default: true)
• datasmell.enableLeakageCheck: Enable data leakage checks (default: true)
• datasmell.enableGPUChecks: Enable GPU memory checks (default: true)

Privacy

DataSmell runs entirely on your local machine using a bundled AST parser. It does not send your code or telemetry to any external servers.

License

MIT