22 citations · 24 across the 7 of their papers we have counts for
6 papers · 1 filter
Characterizing Feedback Statements in Machine Learning Jupyter Notebooks
Arumoy Shome, Lu\is Cruz, Diomidis Spinellis +1
Machine learning development in Jupyter notebooks is iterative and feedback-driven. Practitioners author statements that reveal information about program execution and use it to de…
"It Comes in Notebooks": Changes and Challenges when Operationalizing ML Prototypes
Arumoy Shome, Luís Cruz, Diomidis Spinellis +1
Machine learning practitioners commonly prototype models in computational notebooks before transitioning them to automated production systems. Despite its prevalence, the concrete…
Understanding Feedback Mechanisms in Machine Learning Jupyter Notebooks
Arumoy Shome, Luis Cruz, Diomidis Spinellis +1
The machine learning development lifecycle is characterized by iterative and exploratory processes that rely on feedback mechanisms to ensure data and model integrity. Despite the…
Towards Automatic Translation of Machine Learning Visual Insights to Analytical Assertions
Arumoy Shome, Luis Cruz, Arie van Deursen
We present our vision for developing an automated tool capable of translating visual properties observed in Machine Learning (ML) visualisations into Python assertions. The tool ai…
Towards Understanding Machine Learning Testing in Practise
Arumoy Shome, Luis Cruz, Arie van Deursen
Visualisations drive all aspects of the Machine Learning (ML) Development Cycle but remain a vastly untapped resource by the research community. ML testing is a highly interactive…
Data Smells in Public Datasets
Arumoy Shome, Luis Cruz, Arie van Deursen
The adoption of Artificial Intelligence (AI) in high-stakes domains such as healthcare, wildlife preservation, autonomous driving and criminal justice system calls for a data-centr…