RT Journal Article
T1 Leveraging belief uncertainty for informed decision making in software product line evolution
A1 Horcas-Aguilera, José Miguel
A1 Burgueño-Caballero, Lola
A1 Kienzle, Jörg
K1 Programas de aplicación - Desarrollo
K1 Aplicaciones informáticas - Desarrollo
K1 Software - Desarrollo - Toma de decisones
K1 Python (Lenguaje de programación)
AB Software Product Lines (SPL) are not static software artifacts, but they evolve over time. The planning, realization, and release of a SPL requires many high-level decisions involving many different stakeholders with different expertise. Taking their opinions into account to make the right decisions is not trivial. Currently there are no mechanisms to assist stakeholders in the decision making process in an informed manner. In this paper, we propose the use of belief uncertainty in conjunction with feature models to assist in the evolution of SPLs by explicitly quantifying opinions and their associated uncertainty. We present a novel approach in which subjective logic is used to represent the opinions of stakeholders in three evolution scenarios, namely feature model evolution, next release problem and variability reduction. We apply our approach to the evolution of the Xiaomi MiBand SmartWatch SPL over the time period from July 2014 to October 2023. We present an implementation of our approach and evaluate its scalability.
PB Elsevier
YR 2025
FD 2025-01
LK https://hdl.handle.net/10630/36070
UL https://hdl.handle.net/10630/36070
LA eng
NO Horcas, J. M., Burgueño, L., & Kienzle, J. (2025). Leveraging belief uncertainty for informed decision making in software product line evolution. Journal of Systems and Software, 219, 112235.
NO Data availability:Software Artifact: https://github.com/atenearesearchgroup/fms-subjectivelogic
NO Funding for open access charge: Universidad de Málaga / CBUA.This work was partially funded by the Spanish Government (Ministerio de Ciencia e Innovación–Agencia Estatal de Investigación), Spain under projects PID2021-125527NB-I00, TED2021-130523B-I00, IRIS (PID2021-122812OB-I00) (co-financed by FEDER funds), Data-pl (PID2022-138486OB-I00), and TASOVA PLUS research network(RED2022-134337-T); and by Junta de Andalucía, Spain under project QUAL21 010UMA. Funding, including open access charge: Universidad de Málaga/CBUA, Spain.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 19 ene 2026