Explicit or Implicit? On Feature Engineering for ML-based Variability-intensive Systems - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2023

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. Shall we give up explicit feature management in variability-intensive systems to embrace machine learning advances?
Fichier principal
Vignette du fichier
main.pdf (384.31 Ko) Télécharger le fichier
Origin Files produced by the author(s)

Dates and versions

hal-03890876 , version 1 (30-01-2023)

Licence

Identifiers

Cite

Paul Temple, Gilles Perrouin. Explicit or Implicit? On Feature Engineering for ML-based Variability-intensive Systems. VaMoS 2023 - 17th International Working Conference on Variability Modelling of Software-Intensive Systems, Jan 2023, Odense, Denmark. pp.1-3, ⟨10.1145/3571788.3571804⟩. ⟨hal-03890876⟩
39 View
130 Download

Altmetric

Share

Gmail Mastodon Facebook X LinkedIn More