Reasoning About Variability Models Through Network Analysis

TL;DR

This paper applies network analysis to large datasets of feature models to uncover structural patterns, domain differences, and indicators useful for maintenance and evolution of variability models.

Contribution

It introduces a scalable, graph-based method for analyzing the structure of feature models across diverse domains and sizes, providing empirical insights into their properties.

Findings

Consistent structural traits across models, such as dependency relations and central features.

Domain-specific deviations in structural patterns.

Network indicators can aid in understanding and maintaining variability models.

Abstract

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.