Highly-configurable systems provide significant reuse opportunities by tailoring system variants based on a set of features. Those features can interact in undesired ways which may result in faults. Thus, we propose VarXplorer, a dynamic and iterative approach to detect suspicious interactions. To evaluate whether VarXplorer helps improving the performance of identifying suspicious interactions, we performed two empirical studies. Our results shows that from the VarXplorer graphs, participants are able to identify suspicious interactions more than 3 times faster compared to the state-of-the-art tool. Additionally, the iterative detection process provides a more efficient feature interaction analysis, reducing the data developers needs to check to find problematic interactions.
A comparison of performance specialization learning for configurable systems
