MLScent A tool for Anti-pattern detection in ML projects

• URL: http://arxiv.org/abs/2502.18466v1
• Date: Thu, 30 Jan 2025 11:19:16 GMT
• Title: MLScent A tool for Anti-pattern detection in ML projects
• Authors: Karthik Shivashankar, Antonio Martini,
• Abstract summary: This paper introduces MLScent, a novel static analysis tool for code smell detection.<n>MLScent implements 76 distinct detectors across major machine learning frameworks.<n>Results show high accuracy in framework-specific anti-patterns, data handling issues, and general ML code smells.
• Score: 5.669063174637433
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Machine learning (ML) codebases face unprecedented challenges in maintaining code quality and sustainability as their complexity grows exponentially. While traditional code smell detection tools exist, they fail to address ML-specific issues that can significantly impact model performance, reproducibility, and maintainability. This paper introduces MLScent, a novel static analysis tool that leverages sophisticated Abstract Syntax Tree (AST) analysis to detect anti-patterns and code smells specific to ML projects. MLScent implements 76 distinct detectors across major ML frameworks including TensorFlow (13 detectors), PyTorch (12 detectors), Scikit-learn (9 detectors), and Hugging Face (10 detectors), along with data science libraries like Pandas and NumPy (8 detectors each). The tool's architecture also integrates general ML smell detection (16 detectors), and specialized analysis for data preprocessing and model training workflows. Our evaluation demonstrates MLScent's effectiveness through both quantitative classification metrics and qualitative assessment via user studies feedback with ML practitioners. Results show high accuracy in identifying framework-specific anti-patterns, data handling issues, and general ML code smells across real-world projects.

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