一句话：提出 Comprex 白盒方法，结合局部测量、动态污点分析与配置空间压缩，以更低成本构建精度相当且可解释的性能影响模型。
可配置系统的性能受配置项及其交互影响。现有黑盒方法通过采样与机器学习构建性能影响模型，在测量开销、精度与可解释性之间存在权衡。本文提出 Comprex：一种白盒方法，结合局部测量、动态污点分析以在实现中追踪配置项、组合性以及配置空间压缩，不依赖机器学习外推不完整样本。作者在 4 个广泛使用的开源项目上评估，结果表明 Comprex 构建的性能影响模型与最准确但昂贵的黑盒方法精度相当，成本更低，并额外提供可解释、局部化的模型优势。
Performance-influence models can help stakeholders understand how and where configuration options and their interactions influence the performance of a system. With this understanding, stakeholders can debug performance behavior and make deliberate configuration decisions. Current black-box techniques to build such models combine various sampling and learning strategies, resulting in tradeoffs between measurement effort, accuracy, and interpretability. We present Comprex, a white-box approach to build performance-influence models for configurable systems, combining insights of local measurements, dynamic taint analysis to track options in the implementation, compositionality, and compression of the configuration space, without relying on machine learning to extrapolate incomplete samples. Our evaluation on 4 widely-used, open-source projects demonstrates that Comprex builds similarly accurate performance-influence models to the most accurate and expensive black-box approach, but at a reduced cost and with additional benefits from interpretable and local models.
DOI 原文 · arXiv · PDF（开放获取） · DBLP
@inproceedings{VelezJSAK21,
title = {White-Box Analysis over Machine Learning: Modeling Performance of Configurable Systems},
author = {Miguel Velez and Pooyan Jamshidi and Norbert Siegmund and Sven Apel and Christian Kastner},
booktitle = {ICSE},
year = {2021},
doi = {10.1109/icse43902.2021.00100}
}