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.
