12 citations · 23 across the 12 of their papers we have counts for
13 papers
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…
Learning from Change: Predictive Models for Incident Prevention in a Regulated IT Environment
Eileen Kapel, Jan Lennartz, Luis Cruz +2
Effective IT change management is important for businesses that depend on software and services, particularly in highly regulated sectors such as finance, where operational reliabi…
Framework Matters: Energy Efficiency of UI Automation Testing Frameworks
Timmie M. R. Lagermann, Kristina Sophia Carter, Su Mei Gwen Ho +3
We examine per action energy consumption across four web user interface (UI) automation testing frameworks to determine whether consistent tendencies can guide energy-aware test de…
Sustainable Machine Learning Retraining: Optimizing Energy Efficiency Without Compromising Accuracy
Lorena Poenaru-Olaru, June Sallou, Luis Cruz +2
The reliability of machine learning (ML) software systems is heavily influenced by changes in data over time. For that reason, ML systems require regular maintenance, typically bas…
FedTilt: Towards Multi-Level Fairness-Preserving and Robust Federated Learning
Binghui Zhang, Luis Mares De La Cruz, Binghui Wang
Federated Learning (FL) is an emerging decentralized learning paradigm that can partly address the privacy concern that cannot be handled by traditional centralized and distributed…
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…
Green Runner: A tool for efficient deep learning component selection
Jai Kannan
For software that relies on machine-learned functionality, model selection is key to finding the right model for the task with desired performance characteristics. Evaluating a mod…
McUDI: Model-Centric Unsupervised Degradation Indicator for Failure Prediction AIOps Solutions
Lorena Poenaru-Olaru, Luis Cruz, Jan Rellermeyer +1
Due to the continuous change in operational data, AIOps solutions suffer from performance degradation over time. Although periodic retraining is the state-of-the-art technique to p…
Data vs. Model Machine Learning Fairness Testing: An Empirical Study
Arumoy Shome, Luis Cruz, Arie van Deursen
Although several fairness definitions and bias mitigation techniques exist in the literature, all existing solutions evaluate fairness of Machine Learning (ML) systems after the tr…
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…
Energy Patterns for Web: An Exploratory Study
Pooja Rani, Jonas Zellweger, Veronika Kousadianos +3
As the energy footprint generated by software is increasing at an alarming rate, understanding how to develop energy-efficient applications has become a necessity. Previous work ha…
Is Your Anomaly Detector Ready for Change? Adapting AIOps Solutions to the Real World
Lorena Poenaru-Olaru, Natalia Karpova, Luis Cruz +2
Anomaly detection techniques are essential in automating the monitoring of IT systems and operations. These techniques imply that machine learning algorithms are trained on operati…