Automated Model-Based Assurance Case Management Using Constrained Natural Language

Assurance cases are used to communicate and assess confidence in critical system properties, e.g., safety and security. Historically, assurance cases have been manually created documents, validated by engineers through lengthy and error-prone processes. Recently, system assurance practitioners have begun adopting model-based approaches to improve the efficiency and quality of system assurance activities. This becomes increasingly important, for example, to ensure the safety of robotics and autonomous systems (RASs), as they are adopted into society. Such systems can be highly complex, and so it is a challenge to manage the development life-cycle and improve efficiency, including coordination of validation activities, and change impact analysis in interconnected system assurance artifacts. However, adopting model-based approaches require skills in the model management languages, which system assurance practitioners may not be acquainted with. In this article, we contribute an automated validation framework for the model-based assurance cases, which promotes the usage of a constrained natural language (CNL), that can be automatically transformed and executed against engineering models involved in assurance case development. We apply our approach to a case study based on an autonomous underwater vehicle (AUV).