On the relation of control-flow and performance feature interactions: a case study

Detecting feature interactions is imperative for accurately predicting performance of highly-configurable systems. State-of-the-art performance prediction techniques rely on supervised machine learning for detecting feature interactions, which, in turn, relies on time-consuming performance measurements to obtain training data. By providing information about potentially interacting features, we can reduce the number of required performance measurements and make the overall performance prediction process more time efficient. We expect that information about potentially interacting features can be obtained by analyzing the source code of a highly-configurable system, which is computationally cheaper than performing multiple performance measurements. To this end, we conducted an in-depth qualitative case study on two real-world systems ( mbedTLS and SQLite ), in which we explored the relation between internal (precisely control-flow) feature interactions, detected through static program analysis, and external (precisely performance) feature interactions, detected by performance-prediction techniques using performance measurements. We found that a relation exists that can potentially be exploited to predict performance interactions.