Performance Prediction of Configurable Software Systems by Fourier Learning (T)
Performance Prediction of Configurable Software Systems by Fourier Learning (T)
复制标题
DOI：
10.1109/ase.2015.15
复制
发表时间：
2015-11
期刊：
影响因子：
--
通讯作者：
Yi Zhang;Jianmei Guo;Eric Blais;K. Czarnecki
中科院分区：
文献类型：
--
作者：
Yi Zhang;Jianmei Guo;Eric Blais;K. Czarnecki
了解性能在可配置软件系统的大量变体之间是如何变化的，这对于帮助涉众选择理想的变体非常重要。给定一个具有n个可选特性的软件系统，测量其所有2n个可能的配置以确定其性能通常是不可行的。因此，已经提出了各种技术来基于测量配置的小样本来预测软件性能。我们提出了一种基于傅里叶变换的新算法，该算法能够对任何可配置的软件系统进行预测，并在理论上保证用户指定的准确性和置信度，同时使用最小样本数到常数因子。从实际可配置系统构建的案例研究的经验结果证明了我们的算法的有效性。
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 configurable 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.