Quantum Computing for Feature Model Analysis: Potentials and Challenges

Feature modeling is a technique to model the variability of configurable systems. When working with feature models, it is possible to analyze them, for instance, by counting the number of valid configurations, searching feature model anomalies, or creating samples of configurations for testing. Classical feature model analysis techniques are based on solving algorithmic problems such as boolean satisfiability, satisfiability modulo theories, or integer linear programming. Existing analysis approaches provide satisfactory solutions for small and medium-sized problem instances, but scaling issues are observed for large-sized feature models. Quantum computers provide up to superpolynomial speedups for specific algorithmic problems and have the potential to solve those scaling issues. This paper analyzes the algorithmic techniques used in classical product line analysis and identifies potentials and challenges for quantum speedups. Our findings show that quantum algorithms like QAOA and Grover have the potential to speed up SAT and ILP-based feature model analysis techniques, but only after additional improvements in quantum hardware have been made.