MLScent: A tool for Anti-pattern detection in ML projects
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.
Mon 28 AprDisplayed time zone: Eastern Time (US & Canada) change
14:00 - 15:30 | Quality Assurance for AI systemsResearch and Experience Papers at 208 Chair(s): Eduardo Santana de Almeida Federal University of Bahia | ||
14:00 10mTalk | Towards a Domain-Specific Modeling Language for Streamlined Change Management in AI Systems Development Research and Experience Papers Razan Abualsaud IRIT, CNRS, Toulouse | ||
14:10 15mTalk | An AI-driven Requirements Engineering Framework Tailored for Evaluating AI-Based Software Research and Experience Papers Hamed Barzamini , Fatemeh Nazaritiji Northern Illinois University, Annalise Brockmann Northern Illinois University, Hasan Ferdowsi Northern Illinois university, Mona Rahimi Northern Illinois University | ||
14:25 15mTalk | MLScent: A tool for Anti-pattern detection in ML projects Research and Experience Papers | ||
14:40 15mTalk | Debugging and Runtime Analysis of Neural Networks with VLMs (A Case Study)Distinguished paper Award Candidate Research and Experience Papers Boyue Caroline Hu University of Toronto, Divya Gopinath KBR; NASA Ames, Ravi Mangal Colorado State University, Nina Narodytska VMware Research, Corina S. Păsăreanu Carnegie Mellon University, Susmit Jha SRI | ||
14:55 15mTalk | Investigating Issues that Lead to Code Technical Debt in Machine Learning Systems Research and Experience Papers Rodrigo Ximenes Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Antonio Pedro Santos Alves Pontifical Catholic University of Rio de Janeiro, Tatiana Escovedo Pontifical Catholic University of Rio de Janeiro, Rodrigo Spinola Virginia Commonwealth University, Marcos Kalinowski Pontifical Catholic University of Rio de Janeiro (PUC-Rio) Pre-print | ||
15:10 10mTalk | Addressing Quality Challenges in Deep Learning: The Role of MLOps and Domain Knowledge Research and Experience Papers Santiago del Rey Universitat Politècnica De Catalunya - Barcelona Tech, Adrià Medina Universitat Politècnica de Barcelona - BarcelonaTech (UPC), Xavier Franch Universitat Politècnica de Catalunya, Silverio Martínez-Fernández UPC-BarcelonaTech Pre-print | ||
15:20 10mOther | Discussion Research and Experience Papers |