The rise of AI-based and autonomous systems is raising concerns and apprehension due to potential negative repercussions stemming from their behavior or decisions.
In this paper, we propose to extend the state of the art on architecture framework by providing a mathematical model for system architectures, which is scalable and supports co-evolution of different aspects for example of an AI system.
Development of the intelligent autonomous robot technology presupposes its anticipated beneficial effect on the individuals and societies.
In this work, we propose a goal-oriented assurance process that supports taming different sources (within different classes) of uncertainty from defining the goals at design time to performing self-adaptation at runtime.
Despite all the efforts on assurances for self-adaptive systems at design or runtime, there is still a gap on verifying and validating real-time constraints accounting for context variability.