On the Relation of External and Internal Feature Interactions: A Case Study
This work addresses efficiency in performance prediction for configurable systems, but it is incremental as it builds on existing techniques.
The study tackled the problem of reducing time-consuming performance measurements for predicting feature interactions in configurable systems by exploring the relationship between static code analysis and performance-based detection, finding that a relation exists which could be exploited.
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.