A Software Product Line is a set of software systems of a domain, which share some common features but also have significant variability. A feature model is a variability modeling artifact which represents differences among software products with respect to variability relationships among their features. Having a feature model along with a reference model developed in the domain engineering lifecycle, a concrete product of the family is derived by selecting features in the feature model (referred to as the configuration process) and by instantiating the reference model. However, feature model configuration can be a cumbersome task because: 1) feature models may consist of a large number of features, which are hard to comprehend and maintain; and 2) many factors including technical limitations, implementation costs, stakeholders' requirements and expectations must be considered in the configuration process. Recognizing these issues, a significant amount of research efforts has been dedicated to different aspects of feature model configuration such as automating the configuration process. Several approaches have been proposed to alleviate the feature model configuration challenges through applying visualization and interaction techniques. However, there have been limited empirical insights available into the impact of visualization and interaction techniques on the feature model configuration process. In this paper, we present a set of visualization and interaction interventions for representing and configuring feature models, which are then empirically validated to measure the impact of the proposed interventions. An empirical study was conducted by following the principles of control experiments in software engineering and by applying the well-known software quality standard ISO 9126 to operationalize the variables investigated in the experiment. The results of the empirical study revealed that the employed visualization and interaction interventions significantly improved completion time of comprehension and changing of the feature model configuration. Additionally, according to results, the proposed interventions are easy-to-use and easy-to-learn for the participants.