N-dimensional tensor factorization for self-configuration of software product lines at runtime

Dynamic software product lines demand self-adaptation of their behavior to deal with runtime contextual changes in their environment and offer a personalized product to the user. However, taking user preferences and context into account impedes the manual configuration process, and thus, an efficient and automated procedure is required. To automate the configuration process, context-aware recommendation techniques have been acknowledged as an effective mean to provide suggestions to a user based on their recognized context. In this work, we propose a collaborative filtering method based on tensor factorization that allows an integration of contextual data by modeling an N-dimensional tensor User-Feature-Context instead of the traditional two-dimensional User-Feature matrix. In the proposed approach, different types of non-functional properties are considered as additional contextual dimensions. Moreover, we show how to self-configure software product lines by applying our N-dimensional tensor factorization recommendation approach. We evaluate our approach by means of an empirical study using two datasets of configurations derived for medium-sized product lines. Our results reveal significant improvements in the predictive accuracy of the configuration over a state-of-the-art non-contextual matrix factorization approach. Moreover, it can scale up to a 7-dimensional tensor containing hundred of configurations in a couple of milliseconds.