RT Journal Article
T1 Detecting feature influences to quality attributes in large and partially measured spaces using smart sampling and dynamic learning
A1 Muñoz-Guerra, Daniel Jesús
A1 Pinto-Alarcón, Mónica
A1 Fuentes-Fernández, Lidia
K1 Ingeniería del software
K1 Energía - Medición
AB Emergent application domains (e.g., Edge Computing/Cloud/B5G systems) are complex to be built manually. They are characterised by high variability and are modelled by large Variability Models (VMs), leading to large configuration spaces. Due to the high number of variants present in such systems, it is challenging to find the best-ranked product regarding particular Quality Attributes (QAs) in a short time.Moreover, measuring QAs sometimes is not trivial, requiring a lot of time and resources, as is the case of the energy footprint of software systems — the focus of this paper. Hence, we need a mechanism to analyse how features and their interactions influence energy footprint, but without measuring all configurations. While practical, sampling and predictive techniques base their accuracy on uniform spaces or some initial domain knowledge, which are not always possible to achieve. Indeed, analysingthe energy footprint of products in large configuration spaces raises specific requirements that we explore in this work. This paper presents SAVRUS (Smart Analyser of Variability Requirements in Unknown Spaces), an approach for sampling and dynamic statistical learning without relying on initial domain knowledge of large and partially QA-measured spaces. SAVRUS reports the degree to which features and pairwise interactions influence a particular QA, like energy efficiency. We validate andevaluate SAVRUS with a selection of likewise systems, which define large searching spaces containing scattered measurements.
PB Elsevier
YR 2023
FD 2023
LK https://hdl.handle.net/10630/26310
UL https://hdl.handle.net/10630/26310
LA eng
NO Daniel-Jesus Munoz, Mónica Pinto, Lidia Fuentes, Detecting feature influences to quality attributes in large and partially measured spaces using smart sampling and dynamic learning, Knowledge-Based Systems, Volume 270, 2023, 110558, ISSN 0950-7051, https://doi.org/10.1016/j.knosys.2023.110558. (https://www.sciencedirect.com/science/article/pii/S0950705123003088)
NO Funding for open access charge: Universidad de Málaga / CBUA.This work is supported by the European Union’s H2020 re search and innovation programme under grant agreementDAEMON H2020-101017109, by the projects IRIS PID2021-12281 2OB-I00 (co-financed by FEDER funds), Rhea P18-FR-1081 (MCI/AEI/ FEDER, UE), and LEIA UMA18-FEDERIA-157, and the PRE2019-087496 grant from the Ministerio de Ciencia e Innovación, Spain.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 19 ene 2026