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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 predictionarXiv:1712.07440v2 fatcat:sgrhbrxyqbf3xohmkwq376xh3i