Continuous Exploration of Infinitely Configurable Cyber-Physical Systems for Sample-based Testing (Co-InCyTe)

持续探索用于基于样本的测试的无限可配置网络物理系统 (Co-InCyTe)

• 批准号: 494838636
• 负责人: Professor Dr. Malte Lochau
• 金额: --
• 依托单位: Department Elektrotechnik und Informatik
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/494838636?language=en
• 关键词: Continuous; Exploration; Infinitely; Configurable; Cyber

Today's software comprises up to thousands of configuration options to adjust to diverse requirements, contexts and platforms. Each (Boolean) option doubles the size of the configuration space which makes quality assurance of every individual configuration practically impossible. Sampling strategies bypass this issue by defining criteria and algorithms for selecting preferable small, yet representative subsets of configurations. Unfortunately, recent sampling approaches are not ready for the upcoming era of safety-critical cyber-physical systems (CPS): (1) they are limited to finite configuration spaces, whereas CPS are literally infinitely configurable, (2) they require a configuration model explicitly specifying the valid configuration space which is not available for CPS in practice, (3) they generate samples in a one-shot manner requiring complete knowledge of the configuration space in advance, %which is also infeasible for CPS, (4) they apply black-box selection criteria, whereas effective sampling of CPS is impossible without additional domain knowledge, e.g., non-functional properties. The proposed project contributes a novel sampling methodology comprehensively tackling challenges (1) -- (4). For (1), we propose a novel configuration model integrating two concepts: feature models extended to infinite configuration spaces and trained classifiers to handle particularly complicated configuration constraints. Concerning (2), we adapt techniques for extracting configuration models from sets of known configurations. To tackle (3), we interleave configuration-model extraction and sample selection for continuously exploring unknown configurations. Finally, concerning (4), we use further solution-space knowledge in form of behavioral models and apply family-based analysis to identify critical configurations.

今天的软件包含多达数千种配置选项，以适应不同的需求、环境和平台。每个（布尔）选项都将配置空间的大小加倍，这使得每个单独配置的质量保证实际上是不可能的。抽样策略通过定义标准和算法来选择优选的小而有代表性的配置子集，从而绕过了这个问题。不幸的是，最近的采样方法还没有为即将到来的安全关键型网络物理系统（CPS）时代做好准备：(1)它们限于有限的配置空间，而CPS实际上是无限可配置的；(2)它们需要一个配置模型来明确指定有效的配置空间，而这在实践中是不可用的；(3)它们以一次性的方式生成样本，需要事先完全了解配置空间，这对CPS来说也是不可实现的；(4)它们采用黑盒选择标准。而如果没有额外的领域知识（例如，非功能属性），CPS的有效采样是不可能的。拟议的项目提供了一种新的采样方法，全面解决了挑战(1)-(4)。对于(1)，我们提出了一种新的配置模型，该模型集成了两个概念：扩展到无限配置空间的特征模型和处理特别复杂的配置约束的训练分类器。关于(2)，我们采用了从已知配置集合中提取配置模型的技术。为了解决(3)，我们将配置模型提取和样本选择交叉进行，以不断探索未知配置。最后，关于(4)，我们进一步以行为模型的形式使用解空间知识，并应用基于家庭的分析来识别关键配置。