一句话：提出模态蕴含图预计算中间值，显著加速高度可配置系统的无回溯配置决策传播。
论文关注高度可配置系统中配置决策传播的性能问题。此类系统包含数千个相互依赖的配置选项，决策传播虽能通过计算用户决策所蕴含的值实现无回溯配置，但在大规模系统上耗时较长，容易成为交互式配置与相关分析的瓶颈。为此，作者提出模态蕴含图（modal implication graphs），在传播过程中预先计算并复用中间值，以降低决策传播的开销。评估在120个真实系统上展开，并与现有最先进算法进行比较，结果表明所提方法在性能上取得了显著改进。
Highly-configurable systems encompass thousands of interdependent configuration options, which require a non-trivial configuration process. Decision propagation enables a backtracking-free configuration process by computing values implied by user decisions. However, employing decision propagation for large-scale systems is a time-consuming task and, thus, can be a bottleneck in interactive configuration processes and analyses alike. We propose modal implication graphs to improve the performance of decision propagation by precomputing intermediate values used in the process. Our evaluation results show a significant improvement over state-of-the-art algorithms for 120 real-world systems.
DOI 原文 ·
@inproceedings{paperbot4418,
title = {Propagating configuration decisions with modal implication graphs},
author = {Sebastian Krieter and Thomas Thüm and Sandro Schulze and Reimar Schröter and Gunter Saake},
booktitle = {ICSE},
year = {2018},
doi = {10.1145/3180155.3180159}
}