Performance prediction of configurable software systems by Fourier learning

Abstract—Understanding how performance varies across a large number of variants of a configurable software system is important for helping stakeholders to choose a desirable variant. Given a software system with n optional features, measuring all its 2n possible configurations to determine their performances is usually infeasible. Thus, various techniques have been proposed to predict software performances based on a small sample of measured configurations. We propose a novel algorithm based on Fourier transform that is able to make predictions of any con-figurable software system with theoretical guarantees of accuracy and confidence level specified by the user, while using minimum number of samples up to a constant factor. Empirical results on the case studies constructed from real-world configurable systems demonstrate the effectiveness of our algorithm. I