@article {528,
	title = {Why CART Works for Variability-Aware Performance Prediction? An Empirical Study on Performance Distributions},
	year = {2013},
	month = {04/2013},
	institution = {Generative Software Development Laboratory, University of Waterloo},
	type = {Technical Report},
	address = {Waterloo},
	abstract = {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.},
	issn = {GSDLAB-TR-2013-04-02},
	attachments = {http://gsd.uwaterloo.ca/sites/default/files/GSDTR20130402gjm.pdf},
	author = {Guo, Jianmei and Krzysztof Czarnecki and Apel, Sven and Siegmund, Norbert and W{\k a}sowski, Andrzej}
}