Agentic Generation and Evolution of Knowledge Models
For software engineers dealing with continuously evolving systems, TrustModel offers a vision for maintaining trustworthy knowledge models, though it remains a conceptual framework without empirical validation.
TrustModel proposes an agentic approach to generate and evolve knowledge models (KMs) for complex software systems, addressing the challenge of keeping KMs synchronized with dynamic environments. The paper demonstrates its application in model-based testing and discusses potential for other MDE activities.
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models (KMs) provide a practical basis for such reasoning, but they may themselves become incomplete, inconsistent, or outdated as systems evolve.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.