@inproceedings{e42fb73982694243b9d3c030ea285264,
title = "Explicit or Implicit? On Feature Engineering for ML-based Variability-intensive Systems",
abstract = "Software variability engineering benefits from Machine Learning (ML) to learn e.g., variability-Aware performance models, explore variants of interest and minimize their energy impact. As the number of applications of combining variability with ML grows, we would like to reflect on what is the core to the configuration process in software variability and inference in ML: feature engineering. These disciplines previously managed features explicitly, easing graceful combinations. Now, deep learning techniques derive automatically obscure but efficient features from data.",
keywords = "feature, machine learning, software variability",
author = "Paul Temple and Gilles Perrouin",
note = "Funding Information: Gilles Perrouin is an FNRS Research Associate. This work was partly funded by the EOS-VeriLearn, project number 30992574 of the Fonds de la Recherche Scientifique (F.R.S-FNRS) in Belgium. Publisher Copyright: {\textcopyright} 2023 ACM.",
year = "2023",
month = jan,
day = "25",
doi = "10.1145/3571788.3571804",
language = "English",
series = "ACM International Conference Proceeding Series",
publisher = "ACM Press",
pages = "91--93",
editor = "Cohen, {Myra B.} and Thomas Th{\"u}m and Jacopo Mauro",
booktitle = "Proceedings of the 17th International Working Conference on Variability Modelling of Software-Intensive Systems, VaMoS 2023, Odense, Denmark, January 25-27, 2023",
address = "United States",
}