Performance-Influence Model for Highly Configurable Software with Fourier Learning and Lasso Regression

Many software systems are highly configurable, which provide a large number of configuration options for users to choose from. During the maintenance and operation of these configurable systems, it is important to estimate the system performance under any specific configurations and understand the performance-influencing configuration options. However, it is often not feasible to measure the system performance under all the possible configurations as the combination of configurations could be exponential. In this paper, we propose PerLasso, a performance modeling and prediction method based on Fourier Learning and Lasso (Least absolute shrinkage and selection operator) regression techniques. Using a small sample of measured performance values of a configurable system, PerLasso produces a performance-influence model, which can 1) predict system performance under a new configuration; 2) explain the influence of the individual features and their interactions on the software performance. Besides, to reduce the number of Fourier coefficients to be estimated for large-scale systems, we also design a novel dimension reduction algorithm. Our experimental results on four synthetic and six real-world datasets confirm the effectiveness of our approach. Compared to the existing performance-influence models, our models have higher or comparable prediction accuracy.