@inproceedings{3a355f3bd9b142ce953cbcac1881f0ea,
title = "FMCooler: Simulated annealing for feature model configuration selection",
abstract = "FMCooler is a tool for the automatic optimization in the selection of a configuration from a feature model based on the Simulated Annealing metaheuristic. It is a python-based tool built on top of flamapy, qubovert and dwave-neal. The formulation abstractions and details are exposed, as well as examples of usage to understand the easy handling of the module. We discuss experimentation results to argue that is a valid candidate to be included in the toolset to address this problem given its competitive results and scalability. We also include a discussion on future extensions as the reuse of the abstractions for experimenting with quantum computation. Tool and video: https://github.com/jdanielescanez/fmcooler",
keywords = "AI, feature model, product lines, simulated annealing",
author = "Daniel Escanez-Exposito and Jabier Martinez and Eneko Osaba and Pino Caballero-Gil",
note = "Publisher Copyright: {\textcopyright} 2025 Copyright held by the owner/author(s).; 29th ACM International Systems and Software Product Line Conference, SPLC 2025 ; Conference date: 01-09-2025 Through 05-09-2025",
year = "2025",
month = sep,
day = "1",
doi = "10.1145/3748269.3748488",
language = "English",
series = "SPLC 2025: 29th ACM International Systems and Software Product Line Conference - Proceedings",
publisher = "Association for Computing Machinery, Inc",
pages = "35--38",
editor = "Luaces, \{Miguel R.\} and Rodeiro, \{Tirso V.\} and Sandra Greiner and \{Galindo Duarte\}, Jose and Tao Yue and Kentaro Yoshimura and Laura Semini and Maxime Cordy and Maider Azanza and Jacob Kruger and Gilles Perrouin and Sophie Fortz and Iris Groher and Daniel-Jesus Munoz and Klaus Schmid and Francisca Perez and Jessie Galasso-Carbonnel and Horcas, \{Jose Miguel\} and Kevin Feichtinger",
booktitle = "SPLC 2025",
}