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 T ensorFlow (13 detectors), PyT orch (12 detectors), Scikit-learn (9 detectors), and Hugging Face (10 detectors), along with data science libraries like Pandas and NumPy (8 detectors each). 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. The software development landscape has undergone a dramatic transformation with the integration of Machine Learning (ML). Recent statistics from Gartner highlight this shift, revealing a striking 270% increase in ML adoption within enterprise software projects over the last four years [1]. This rapid adoption, however, brings its own set of complexities. Traditional software development practices have had to evolve significantly to accommodate ML's unique requirements, including the need for extensive datasets, sophisticated algorithms, and iterative development cycles [3]. These fundamental differences have catalyzed a complete reimagining of software development methodologies, from initial design through testing and maintenance [4], [5] which is also highlighted by Tang et al. [6] in their empirical study of ML systems refactoring and technical debt. ML projects introduce distinct code quality challenges that set them apart from conventional software development. The complexity stems from their inherent characteristics: intricate mathematical operations, extensive data preprocessing requirements, and sophisticated model architectures that challenge traditional code maintenance approaches [7].