一句话：提出可变性模型的概率基础，支持统计推理、可视化与更精确敏感的自动化分析。
高度可定制软件常用可变性模型描述可配置特征及其依赖与冲突，但此类模型可包含数千特征，理解与自动分析困难。本文提出一种新颖的概率基础，用于对可变性模型进行统计推理，并据此提供可视化、描述和解释模型的新方法。该方法还能改进软件产品线中现有分析技术，在以往仅有近似结果处给出精确计算，并提升已有分析操作的敏感度。作者在真实案例上评估，模型最多含17,365个特征，覆盖KConfig与特征模型两种语言，展示了方法优势。
Variability models are broadly used to specify the configurable features of highly customizable software. In practice, they can be large, defining thousands of features with their dependencies and conflicts. In such cases, visualization techniques and automated analysis support are crucial for understanding the models. This paper contributes to this line of research by presenting a novel, probabilistic foundation for statistical reasoning about variability models. Our approach not only provides a new way to visualize, describe and interpret variability models, but it also supports the improvement of additional state-of-the-art methods for software product lines; for instance, providing exact computations where only approximations were available before, and increasing the sensitivity of existing analysis operations for variability models. We demonstrate the benefits of our approach using real case studies with up to 17,365 features, and written in two different languages (KConfig and feature models).
@inproceedings{HeradioFME19,
title = {Supporting the Statistical Analysis of Variability Models},
author = {Ruben Heradio and David Fernandez-Amoros and Christoph Mayr-Dorn and Alexander Egyed},
booktitle = {ICSE},
year = {2019},
doi = {10.1109/icse.2019.00091}
}