ML Code Smells: From Specification to Detection

arXiv:2509.20491v2 Announce Type: replace-cross Abstract: The rapid adoption of Artificial Intelligence (AI) is increasingly realised through Machine Learning (ML) pipelines that integrate data preprocessing, model training, evaluation scripts, and configuration-heavy experimentation code. In these ML-based systems, small and often overlooked implementation choices can silently compromise experimental reproducibility, robustness to data and environment changes, and maintainability. We study ML c