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Customizable programs and program families provide user-selectable features to allow users to tailor a program to an application scenario. Knowing in advance which feature selection yields the best performance is difficult because a direct measurement of all possible feature combinations is infeasible. The authors' work aims at predicting program performance based on selected features. However, when features interact, accurate predictions are challenging. An interaction occurs when a particular feature combination has an unexpected influence on performance. They present a method that automatically detects performance-relevant feature interactions to improve prediction accuracy. To this end, they propose three heuristics to reduce the number of measurements required to detect interactions.
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