A Context-Aware Recommender System for Extended Software Product Line Configurations

Juliana Alves Pereira, Sandro Schulze, Sebastian Krieter, Márcio Ribeiro, Gunter Saake

Abstract: Mass customization of standardized products has become a trend to succeed in today's market environment. Software Product Lines (SPLs) address this trend by describing a family of software products that share a common set of features. However, choosing the appropriate set of features that matches a user's individual interests is hampered due to the overwhelming amount of possible SPL configurations. Recommender systems can address this challenge by filtering the number of configurations and suggesting a suitable set of features for the user's requirements. In this paper, we propose a context-aware recommender system for predicting feature selections in an extended SPL configuration scenario, i.e. taking nonfunctional properties of features into consideration. We present an empirical evaluation based on a large real-world dataset of configurations derived from industrial experience in the Enterprise Resource Planning domain. Our results indicate significant improvements in the predictive accuracy of our context-aware recommendation approach over a state-of-the-art binary-based approach.