Empirical comparison of regression methods for variability-aware performance prediction

Abstract

Product line engineering derives product variants by selecting features. Understanding the correlation between feature selection and performance is important for stakeholders to acquire a desirable product variant. We infer such a correlation using four regression methods based on small samples of measured configurations, without additional effort to detect feature interactions. We conduct experiments on six real-world case studies to evaluate the prediction accuracy of the regression methods. A key finding in our empirical study is that one regression method, called Bagging, is identified as the best to make accurate and robust predictions for the studied systems.