Model Based System Assurance Using the Structured Assurance Case Metamodel
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Abstract (arXiv)
Assurance cases are used to demonstrate confidence in system properties of interest (e.g. safety and/or security). A number of system assurance approaches are adopted by industries in the safety-critical domain. However, the task of constructing assurance cases remains a manual, trivial and informal process. The Structured Assurance Case Metamodel (SACM) is a standard specified by the Object Management Group (OMG). SACM provides a richer set of features than existing system assurance languages/approaches. SACM provides a foundation for model-based system assurance, which has great potentials in growing technology domains such as Open Adaptive Systems. However, the intended usage of SACM has not been sufficiently explained. In addition, there has been no support to interoperate between existing assurance case (models) and SACM models. In this article, we explain the intended usage of SACM based on our involvement in the OMG specification process of SACM. In addition, to promote a model-based approach, we provide SACM compliant metamodels for existing system assurance approaches (the Goal Structuring Notation and Claims-Arguments-Evidence), and the transformations from these models to SACM. We also briefly discuss the tool support for model-based system assurance which helps practitioners to make the transition from existing system assurance approaches to model-based system assurance using SACM.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 May 2019.
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Citing papers per year: 2023: 1; 2024: 0; 2025: 0; 2026: 3.
4 papers.
- 2026Model-Driven Discipline for Multi-Agent LLMs: Requirement-to-Verification Generation of Traceable System Modelscited by 1
- 2023Operationalizing Assurance Cases for Data Scientists: A Showcase of Concepts and Tooling in the Context of Test Data Quality for Machine Learningcited by 0
- 2026Safety Case Patterns for VLA-based driving systems: Insights from SimLingocited by 0
- 2026AI Sandboxes: A Threat Model, Taxonomy, and Measurement Frameworkcited by 0
- 2026Model-Driven Discipline for Multi-Agent LLMs: Requirement-to-Verification Generation of Traceable System Models18 Jul 2026
- 2026AI Sandboxes: A Threat Model, Taxonomy, and Measurement Framework16 Jun 2026
- 2026Safety Case Patterns for VLA-based driving systems: Insights from SimLingo16 Mar 2026
- 2023Operationalizing Assurance Cases for Data Scientists: A Showcase of Concepts and Tooling in the Context of Test Data Quality for Machine Learning8 Dec 2023