28 citations · 56 across the 12 of their papers we have counts for
12 papers
Big Questions on Software Architecture: Report of the ICSE 2026 BoF on Software Architecture
Davide Taibi, Patricia Lago, Henry Muccini
Software Architecture (SA) is more important than ever, from the need to master the pervasive impact of technology in society and planet earth, to balancing the role of humans vers…
Attention to Detail: Evaluating Energy, Performance, and Accuracy Trade-offs Across vLLM Configurations
Nada Zine, Tristan Coignion, Vincenzo Stoico +4
Large Language Models are reshaping how software is developed and maintained. They are typically deployed in production using inference engines such as vLLM, which can efficiently…
Monitoring and Observability of Machine Learning Systems: Current Practices and Gaps
Joran Leest, Ilias Gerostathopoulos, Patricia Lago +1
Production machine learning (ML) systems fail silently -- not with crashes, but through wrong decisions. While observability is recognized as critical for ML operations, there is a…
Tracing Distribution Shifts with Causal System Maps
Joran Leest, Ilias Gerostathopoulos, Patricia Lago +1
Monitoring machine learning (ML) systems is hard, with standard practice focusing on detecting distribution shifts rather than their causes. Root-cause analysis often relies on man…
Greening AI-enabled Systems with Software Engineering: A Research Agenda for Environmentally Sustainable AI Practices
Luís Cruz, João Paulo Fernandes, Maja H. Kirkeby +22
The environmental impact of Artificial Intelligence (AI)-enabled systems is increasing rapidly, and software engineering plays a critical role in developing sustainable solutions.…
From Tea Leaves to System Maps: A Survey and Framework on Context-aware Machine Learning Monitoring
Joran Leest, Claudia Raibulet, Patricia Lago +1
Machine learning (ML) models in production fail when their broader systems -- from data pipelines to deployment environments -- deviate from training assumptions, not merely due to…
Insights into resource utilization of code small language models serving with runtime engines and execution providers
Francisco Durán, Matias Martinez, Patricia Lago +1
The rapid growth of language models, particularly in code generation, requires substantial computational resources, raising concerns about energy consumption and environmental impa…
The More the Merrier? Navigating Accuracy vs. Energy Efficiency Design Trade-Offs in Ensemble Learning Systems
Rafiullah Omar, Justus Bogner, Henry Muccini +3
Background: Machine learning (ML) model composition is a popular technique to mitigate shortcomings of a single ML model and to design more effective ML-enabled systems. While ense…
An Analysis of MLOps Architectures: A Systematic Mapping Study
Faezeh Amou Najafabadi, Justus Bogner, Ilias Gerostathopoulos +1
Context. Despite the increasing adoption of Machine Learning Operations (MLOps), teams still encounter challenges in effectively applying this paradigm to their specific projects.…
How to Sustainably Monitor ML-Enabled Systems? Accuracy and Energy Efficiency Tradeoffs in Concept Drift Detection
Rafiullah Omar, Justus Bogner, Joran Leest +3
ML-enabled systems that are deployed in a production environment typically suffer from decaying model prediction quality through concept drift, i.e., a gradual change in the statis…
Expert-Driven Monitoring of Operational ML Models
Joran Leest, Claudia Raibulet, Ilias Gerostathopoulos +1
We propose Expert Monitoring, an approach that leverages domain expertise to enhance the detection and mitigation of concept drift in machine learning (ML) models. Our approach sup…
A Synthesis of Green Architectural Tactics for ML-Enabled Systems
Heli Järvenpää, Patricia Lago, Justus Bogner +3
The rapid adoption of artificial intelligence (AI) and machine learning (ML) has generated growing interest in understanding their environmental impact and the challenges associate…