ML Anthology

Introducing Pyra: A High-Level Linter for Data Science Software

Greta Dolcetti, Vincenzo Arceri, Antonella Mensi, Enea Zaffanella, Caterina Urban, Agostino Cortesi
ECML-PKDD 2025 pp. 449-453
doi:10.1007/978-3-032-06129-4_29 /ecmlpkdd/2025/dolcetti2025ecmlpkdd-introducing/

Abstract

We present Pyra , a static analysis tool that aims at detecting code smells in data science workflows. Our goal is to capture potential issues, focusing on misleading visualizations, challenges for reproducibility, as well as misleading, unreliable or unexpected results. Link to the demo: https://www.youtube.com/watch?v=D-AsyuhsTyo GitHub repository: https://github.com/spangea/Pyra .

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Cite

Text

Dolcetti et al. "Introducing Pyra: A High-Level Linter for Data Science Software." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2025. doi:10.1007/978-3-032-06129-4_29

Markdown

[Dolcetti et al. "Introducing Pyra: A High-Level Linter for Data Science Software." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2025.](https://mlanthology.org/ecmlpkdd/2025/dolcetti2025ecmlpkdd-introducing/) doi:10.1007/978-3-032-06129-4_29

BibTeX

@inproceedings{dolcetti2025ecmlpkdd-introducing,
  title     = {{Introducing Pyra: A High-Level Linter for Data Science Software}},
  author    = {Dolcetti, Greta and Arceri, Vincenzo and Mensi, Antonella and Zaffanella, Enea and Urban, Caterina and Cortesi, Agostino},
  booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
  year      = {2025},
  pages     = {449-453},
  doi       = {10.1007/978-3-032-06129-4_29},
  url       = {https://mlanthology.org/ecmlpkdd/2025/dolcetti2025ecmlpkdd-introducing/}
}