@inproceedings{d5235ebbee554f47ada2a6e07448378c,
title = "Transferring performance between distinct configurable systems: A case study",
abstract = "Many research studies predict the performance of configurable software using machine learning techniques, thus requiring large amounts of data. Transfer learning aims to reduce the amount of data needed to train these models and has been successfully applied on different executing environments (hardware) or software versions. In this paper we investigate for the first time the idea of applying transfer learning between distinct configurable systems. We design a study involving two video encoders (namely x264 and x265) coming from different code bases. Our results are encouraging since transfer learning outperforms traditional learning for two performance properties (out of three). We discuss the open challenges to overcome for a more general application.",
author = "Luc Lesoil and Hugo Martin and Mathieu Acher and Arnaud Blouin and J{\'e}z{\'e}quel, \{Jean Marc\}",
note = "Publisher Copyright: {\textcopyright} 2022 Copyright held by the owner/author(s).; 16th International Working Conference on Variability Modelling of Software-Intensive Systems, VaMoS 2022 ; Conference date: 23-02-2022 Through 25-02-2022",
year = "2022",
month = feb,
day = "23",
doi = "10.1145/3510466.3510486",
language = "English",
series = "ACM International Conference Proceeding Series",
publisher = "Association for Computing Machinery",
editor = "Paolo Arcaini and Xavier Devroey and Alessandro Fantechi",
booktitle = "Proceedings - VaMoS 2022",
}