A mathematical model of performance-relevant feature interactions

Modern software systems have grown significantly in their size and complexity, therefore understanding how software systems behave when there are many configuration options, also called features, is no longer a trivial task. This is primarily due to the potentially complex interactions among the features. In this paper, we propose a novel mathematical model for performance-relevant, or quantitative in general, feature interactions, based on the theory of Boolean functions. Moreover, we provide two algorithms for detecting all such interactions with little measurement effort and potentially guaranteed accuracy and confidence level. Empirical results on real-world configurable systems demonstrated the feasibility and effectiveness of our approach.