Software product lines (SPL) dynamic configuring process could use methods for recommendation system (RS) elaboration. An overview and analysis of such methods was done in this paper. SPL represent a set of software systems that have common and variable functional components and use a set of paradigms and methods for development. In the classical static SPL the process of configuring performed before executing and performing in the operation environment (OE), in contrast dynamic software product lines performs after executing in the OE. Through the use of which it is possible to customize software solutions in accordance with the needs of end users. The following possible methods to build RS were considered: clustering, Markov decision-making process, matrix factorization. According to the review of the intelligent RS method development and researching of the functionalities of such systems in some open-source projects it was proposed to use N-dimensional context-dependent tensor factorization method and CARSkit tool system. Functional requirements and software architecture of the RS were developed. It allows to automatize software components configuration in the „Smart Home” (SH) systems that could be implemented with CARSkit software toolkit and algorithms implemented with programming language Python. This implementation allows to build a process for tracking changes in the external environment and transfer information to the SH system and, after analyzing the input data, process it in the RS to track changes in the context information. In the future research some additional quantitative experiments will be performed considering the specifics of the SH systems, additionally quantitative metrics will be used for efficiency assessment of the tensor factorization algorithms to predict the dynamic configurations of software components in these systems.
Keywords: recommendation system, software product line, variability, dynamic configuration, architecture.
Coordination of Dynamic Software Components with JavaBIP