@article {484,
	title = {Variability-Aware Performance Modeling: A Statistical Learning Approach},
	year = {2012},
	month = {08/2012},
	institution = {Generative Software Development Laboratory, University of Waterloo},
	type = {Technical Report},
	address = {Waterloo},
	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.},
	issn = {GSDLAB-TR-2012-08-18},
	attachments = {http://gsd.uwaterloo.ca/sites/default/files/GSDTR20120818gjm.pdf},
	author = {Guo, Jianmei and Krzysztof Czarnecki and Apel, Sven and Siegmund, Norbert and W{\k a}sowski, Andrzej}
}