133 citations · 273 across the 8 of their papers we have counts for
19 papers
A Preliminary Investigation of MLOps Practices in GitHub
Fabio Calefato, Filippo Lanubile, Luigi Quaranta
Background. The rapid and growing popularity of machine learning (ML) applications has led to an increasing interest in MLOps, that is, the practice of continuous integration and d…
Pynblint: a Static Analyzer for Python Jupyter Notebooks
Luigi Quaranta, Fabio Calefato, Filippo Lanubile
Jupyter Notebook is the tool of choice of many data scientists in the early stages of ML workflows. The notebook format, however, has been criticized for inducing bad programming p…
Eliciting Best Practices for Collaboration with Computational Notebooks
Luigi Quaranta, Fabio Calefato, Filippo Lanubile
Despite the widespread adoption of computational notebooks, little is known about best practices for their usage in collaborative contexts. In this paper, we fill this gap by elici…
What Makes Agile Software Development Agile?
Marco Kuhrmann, Paolo Tell, Regina Hebig +44
Together with many success stories, promises such as the increase in production speed and the improvement in stakeholders' collaboration have contributed to making agile a transfor…
KGTorrent: A Dataset of Python Jupyter Notebooks from Kaggle
Luigi Quaranta, Fabio Calefato, Filippo Lanubile
Computational notebooks have become the tool of choice for many data scientists and practitioners for performing analyses and disseminating results. Despite their increasing popula…
Towards Productizing AI/ML Models: An Industry Perspective from Data Scientists
Filippo Lanubile, Fabio Calefato, Luigi Quaranta +3
The transition from AI/ML models to production-ready AI-based systems is a challenge for both data scientists and software engineers. In this paper, we report the results of a work…