Despite the huge spread and economical importance of configurable software systems, there is unsatisfactory support in utilizing the full potential of these systems with respect to finding performance-optimal configurations. Prior work on predicting the performance of software configurations suffered from either (a) requiring far too many sample configurations or (b) large variances in their predictions. Both these problems can be avoided using the WHAT spectral learner. WHAT’s innovation is the use of the spectrum (eigenvalues) of the distance matrix between the configurations of a configurable software system, to perform dimensionality reduction. Within that reduced configuration space, many closely associated configurations can be studied by executing only a few sample configurations. For the subject systems studied here, a few dozen samples yield accurate and stable predictors—less than 10% prediction error, with a standard deviation of less than 2%. When compared to the state of the art, WHAT (a) requires 2–10 times fewer samples to achieve similar prediction accuracies, and (b) its predictions are more stable (i.e., have lower standard deviation). Furthermore, we demonstrate that predictive models generated by WHAT can be used by optimizers to discover system configurations that closely approach the optimal performance.

中文翻译：

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通过频谱学习更快地发现更快的系统配置

尽管可配置软件系统具有广泛的传播性和经济重要性，但在利用这些系统的全部潜力来寻找性能最佳配置方面的支持并不令人满意。之前在预测软件配置性能方面的工作受到以下两种情况的影响：(a) 需要太多的样本配置或 (b) 预测差异很大。使用 WHAT 谱学习器可以避免这两个问题。WHAT 的创新之处在于使用可配置软件系统的配置之间的距离矩阵的频谱（特征值）来执行降维。在减少的配置空间内，可以通过仅执行几个示例配置来研究许多密切相关的配置。对于这里研究的学科系统，几十个样本产生准确而稳定的预测因子——预测误差小于 10%，标准偏差小于 2%。与现有技术相比，WHAT (a) 需要 2-10 倍的样本来实现类似的预测精度，并且 (b) 其预测更稳定（即具有更低的标准偏差）。此外，我们证明了优化器可以使用由 WHAT 生成的预测模型来发现最接近最佳性能的系统配置。