Complex software systems such as autonomous vehicles, robotics increasingly interact with dynamic physical, cyber, and social environments. Reasoning about their behavior, maintaining them under continuous change, and evolving them safely require trustworthy knowledge about the system, its assumptions, and its operating context. Knowledge models (KMs) provide a practical basis for such reasoning, but they may themselves become incomplete, inconsistent, or outdated as systems evolve. This paper presents TrustModel, a vision for the agentic generation and evolution of living KMs. TrustModel comprises three agentic subsystems: Modeling, for constructing and updating KMs; Conformance, for assessing their alignment with the system and its environment; and Evolution, for generating guidance to keep KMs synchronized with emerging changes. We demonstrate how TrustModel can be instantiated for model-based testing and discuss its potential for supporting other MDE activities, such as requirements and assumption monitoring, architectural drift tracking, and change impact assessment. Overall, TrustModel positions living KMs as a foundation for dependable engineering of continuously evolving software systems.

翻译：诸如自动驾驶车辆、机器人等复杂软件系统日益与动态的物理、网络及社会环境交互。对其行为进行推理、在持续变更中维护系统以及安全地演化系统，需要关于系统本身、其假设及运行环境的可信知识。知识模型为这类推理提供了实用基础，但随着系统演化，这些模型本身可能变得不完整、不一致或过时。本文提出TrustModel——一种面向自主体生成与演化动态知识模型的愿景。TrustModel包含三个自主体子系统：建模子系统用于构建和更新知识模型；合规性子系统用于评估模型与系统及环境的一致性；演化子系统用于生成指导，使知识模型与新兴变更保持同步。我们展示了如何将TrustModel实例化以支持基于模型的测试，并探讨了其在其他模型驱动工程活动中的潜力，例如需求与假设监控、架构漂移追踪以及变更影响评估。总之，TrustModel将动态知识模型定位为持续演化软件系统可信赖工程的基石。