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• W79667711 abstract "Customizable software systems allow users to derive configurations by selecting features. Building a performance model to understand the tradeoff between performance and feature selection is important to be able to derive a desired configuration. A challenge is to predict performance accurately when features interact. Another is that, in practice, we can often measure only few configurations as a sample for prediction, and we cannot select these configurations freely to cover certain feature interactions. We propose an incremental and variability-aware approach to performance modeling based on statistical learning. Our approach incorporates performance-relevant feature interactions and quantifies their influence implicitly during the process of performance modeling. It identifies the most relevant feature selections automatically for performance prediction. Empirical results on six real-world case studies show that our approach achieves an average of 94% prediction accuracy measuring few randomly selected configurations." @default.
• W79667711 created "2016-06-24" @default.
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• W79667711 date "2012-01-01" @default.
• W79667711 modified "2023-09-26" @default.
• W79667711 title "Variability-Aware Performance Modeling: A Statistical Learning Approach" @default.
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• W79667711 hasPublicationYear "2012" @default.
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