Title: On the Relation of External and Internal Feature Interactions: A Case Study,
Abstract: 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 the information about potentially interacting features can be obtained by
statically analyzing the source code of a highly-configurable system, which is
computationally cheaper than performing multiple performance measurements. To
this end, we conducted a qualitative case study in which we explored the
relation between control-flow feature interactions (detected through static
program analysis) and performance feature interactions (detected by performance
prediction techniques using performance measurements). We found that a relation
exists, which can potentially be exploited to predict performance interactions.