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Why CART Works for Variability-Aware Performance Prediction? An Empirical Study on Performance Distributions
|Title||Why CART Works for Variability-Aware Performance Prediction? An Empirical Study on Performance Distributions|
|Year of Publication||2013|
|Authors||Guo, J., K. Czarnecki, S. Apel, N. Siegmund, and A. Wąsowski|
|Institution||Generative Software Development Laboratory, University of Waterloo|
This report presents follow-up work for our previous technical report "Variability-Aware Performance Modeling: A Statistical Learning Approach" (GSDLAB-TR-2012-08-18). We try to give evidence why our approach, based on a statistical-learning technique called Classification And Regression Trees (CART), works for variability-aware performance prediction. To this end, we conduct a comparative analysis of performance distributions on the evaluated case studies and empirically explore why our approach works with small random samples.