Reasoning About Variability Models Through Network Analysis
This provides a scalable, graph-based foundation for empirical analysis of variability models, addressing a gap in software engineering for researchers and practitioners, though it is incremental as it applies existing network methods to a new context.
The paper tackled the lack of systematic study of structural properties in feature models by analyzing 5,709 models from 20 repositories using network analysis, revealing consistent structural traits and domain-specific deviations that aid in maintenance and modular decomposition.
Feature models are widely used to capture the configuration space of software systems. Although automated reasoning has been studied for detecting problematic features and supporting configuration tasks, significantly less attention has been given to the systematic study of the structural properties of feature models at scale. The approach fills this gap by examining the models' structure through a network analysis perspective. We focus on three Research Questions concerning (i) the structural patterns exhibited by these graphs, (ii) the extent to which such patterns vary across domains and model sources, and (iii) the usefulness of network-based indicators for understanding, maintaining, and evolving variability models. To answer these questions, we analyze a dataset of 5,709 models from 20 repositories, spanning multiple application domains and varying sizes (ranging from 99 to 35,907 variables on their Boolean translation). To do so, graphs of transitive dependencies and conflicts between features are computed. Our results reveal consistent structural traits (e.g., the predominance of dependency relations, the presence of highly central features, or characteristic node degree distributions) as well as notable domain-specific deviations. These findings ease the identification of maintenance-relevant features, opportunities for modular decomposition, and indicators of structural fragility. This approach provides a scalable, graph-based foundation for the empirical analysis of variability models and contributes quantitative evidence to support future research on their structure and evolution.