RT Journal Article
T1 ProDSPL: Proactive self-adaptation based on Dynamic Software Product Lines
A1 Ayala-Viñas, Inmaculada
A1 Alessandro V., Papadopoulos
A1 Amor-Pinilla, María Mercedes
A1 Fuentes-Fernández, Lidia
K1 Ingeniería del software
AB Dynamic Software Product Lines (DSPLs) are a well-accepted approach to self-adaptation at runtime. In the context of DSPLs, there are plenty of reactive approaches that apply countermeasures as soon as a context change happens. In this paper we propose a proactive approach, ProDSPL, that exploits an automatically learnt model of the system, anticipates future variations of the system and generates the best DSPL configuration that can lessen the negative impact of future events on the quality requirements of the system. Predicting the future fosters adaptations that are good for a longer time and therefore reduces the number of reconfigurations required, making the system more stable.ProDSPL formulates the problem of the generation of dynamic reconfigurations as a proactive controller over a prediction horizon, which includes a mapping of the valid configurations of the DSPL into linear constraints. Our approach is evaluated and compared with a reactive approach, DAGAME, also based on a DSPL, which uses a genetic algorithm to generate quasi-optimal feature model configurations at runtime. ProDSPL has been evaluated using a strategy mobile game and a set of randomly generated feature models. The evaluation shows that ProDSPL gives good results with regard to the quality of the configurations generated when it tries anticipate future events. Moreover, in doing so, ProDSPL enforces the system to make as few reconfigurations as possible.
PB ScienceDirect
YR 2021
FD 2021-05
LK https://hdl.handle.net/10630/37363
UL https://hdl.handle.net/10630/37363
LA eng
NO Inmaculada Ayala, Alessandro V. Papadopoulos, Mercedes Amor, Lidia Fuentes, ProDSPL: Proactive self-adaptation based on Dynamic Software Product Lines, Journal of Systems and Software, Volume 175, 2021, 110909, ISSN 0164-1212, https://doi.org/10.1016/j.jss.2021.110909. (https://www.sciencedirect.com/science/article/pii/S0164121221000066)
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 19 ene 2026