EASE 2025
Tue 17 - Fri 20 June 2025 Istanbul, Turkey
Fri 20 Jun 2025 16:15 - 16:30 at Glass Room - Maintenance and Performance Analysis Chair(s): Alexandros Tsakpinis
Configuring the Linux kernel to meet specific requirements, such as binary size, is highly challenging due to its immense complexity—with over 15,000 interdependent options evolving rapidly across different versions. Although several studies have explored sampling strategies and machine learning methods to understand and predict the impact of configuration options, the literature still lacks a comprehensive and large-scale dataset encompassing multiple kernel versions along with detailed quantitative measurements. To bridge this gap, we introduce TuxKConfig, an accessible collection of kernel configurations spanning several kernel releases, specifically from versions 4.13 to 5.8. This dataset, gathered through automated tools and build processes, comprises over 240,000 kernel configurations systematically labeled with compilation outcomes and binary sizes. By providing detailed records of configuration evolution and capturing the intricate interplay among kernel options, our dataset enables innovative research in feature subset selection, prediction models based on machine learning, and transfer learning across kernel versions. Throughout this paper, we describe how the dataset has been made easily accessible via OpenML and illustrate how it can be leveraged using only a few lines of Python code to evaluate AI-based techniques, such as supervised machine learning. We anticipate that this dataset will significantly enhance reproducibility and foster new insights into configuration-space analysis at a scale that presents unique opportunities and inherent challenges, thereby advancing our understanding of the Linux kernel’s configurability and evolution.

Fri 20 Jun

Displayed time zone: Athens change

15:30 - 17:00
Maintenance and Performance AnalysisAI Models / Data / Research Papers at Glass Room
Chair(s): Alexandros Tsakpinis fortiss GmbH
15:30
15m
Talk
Seamless Data Migration between Database Schemas with DAMI-Framework: An Empirical Study on Developer Experience
Research Papers
Delfina Ramos-Vidal Universidade da Coruña, Alejandro Cortiñas Universidade da Coruña, Miguel Rodríguez Luaces Universidade da Coruña, CITIC, Database Lab, Oscar Pedreira Universidade da Coruna, Ángeles Saavedra Places Universidade da Coruña, Wesley Assunção North Carolina State University
Link to publication Pre-print
15:45
15m
Talk
An Empirical Study on the Performance and Energy Usage of Compiled Python Code
Research Papers
Vincenzo Stoico Vrije Universiteit Amsterdam, Andrei Calin Dragomir Vrije Universiteit Amsterdam, Patricia Lago Vrije Universiteit Amsterdam
Pre-print
16:00
15m
Talk
Exploring Performance of Configurable Software Systems: the JHipster Case Study
Research Papers
Edouard Guegain Université de Lille, Alexandre Bonvoisin inria, Mathieu Acher Univ Rennes, Inria, CNRS, IRISA, Clément Quinton University of Lille, Romain Rouvoy Univ. Lille / Inria / IUF
16:15
15m
Talk
Linux Kernel Configurations at Scale: A Dataset for Performance and Evolution Analysis
AI Models / Data
Heraldo Pimenta Borges Filho University of Rennes - Inria - CNRS - IRISA, Juliana Alves Pereira Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Djamel Eddine Khelladi CNRS, IRISA, University of Rennes, Mathieu Acher Univ Rennes, Inria, CNRS, IRISA
Pre-print
16:30
15m
Talk
The Impact of Environment Configurations on the Stability of AI-Enabled Systems
Research Papers
Musfiqur Rahman Concordia University, Montreal, SayedHassan Khatoonabadi Concordia University, Montreal, Ahmad Abdellatif University of Calgary, Haya Samaana An-Najah National University, Emad Shihab Concordia University, Montreal
Pre-print
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