标题： 面向软件产品线的对抗配置 显示英文标题

标题： Towards Adversarial Configurations for Software Product Lines

摘要： 确保软件产品线（SPL）的所有所谓有效配置都能生成结构良好且可接受的产品具有挑战性，因为通常不现实去枚举和测试SPL中的所有单独产品。 最近已经使用机器学习分类器来预测与未见过的配置相关的产品的可接受性。 对于某些配置，其特征值的微小变化可能会使它们根据用户的需求从可接受变为不可接受，反之亦然。 在本文中，我们引入了利用这些特定配置及其在特征空间中的位置来改进分类器，从而改进SPL的工程设计的想法。 从一个可变性模型出发，我们提出使用对抗机器学习技术，通过修改其特征值，从已知的配置中创建新的对抗性配置。 使用一个工业视频生成器，我们展示了对抗性配置不仅可以改进分类器，还可以改进可变性模型、可变性实现和测试预言。 显示英文摘要

摘要： Ensuring that all supposedly valid configurations of a software product line (SPL) lead to well-formed and acceptable products is challenging since it is most of the time impractical to enumerate and test all individual products of an SPL. Machine learning classifiers have been recently used to predict the acceptability of products associated with unseen configurations. For some configurations, a tiny change in their feature values can make them pass from acceptable to non-acceptable regarding users' requirements and vice-versa. In this paper, we introduce the idea of leveraging these specific configurations and their positions in the feature space to improve the classifier and therefore the engineering of an SPL. Starting from a variability model, we propose to use Adversarial Machine Learning techniques to create new, adversarial configurations out of already known configurations by modifying their feature values. Using an industrial video generator we show how adversarial configurations can improve not only the classifier, but also the variability model, the variability implementation, and the testing oracle.