Overview: This paper introduces the STRATA model-based systems engineering (MBSE) methodology, emphasizing its key aspects and unique advantages in verification, validation, test, and evaluation domains. Attendees will gain insights into its applicability and advantages in complex modeling scenarios.
Context: Navigating the initial stages of MBSE model formulation in any tool can be challenging, particularly within collaborative teams. Implementing a methodology offers a structured roadmap, enhancing model organization, usability, and collaboration. The STRATA methodology addresses these challenges, and combined with its model validation rulesets, provides the user feedback on model consistency and completeness.
Purpose: The STRATA methodology is structured in terms of defined levels or layers of the system architecture (the rows) and systems engineering, program management, and specialty engineering concept groups (the columns) affords.. The research aims to elucidate the benefits of adopting the STRATA methodology, particularly in the context of verification and validation. By defining clear model objectives and scope, the STRATA framework allows a modeling team to model what they know regardless of where it fits in the matrix. This supports the early involvement of test and evaluation teams in the requirements refinement and systems architecture definition processes. We will show how the STRATA methodology supports multiple engineering design scenarios.
Approach: Utilizing the GENSYS MBSE tool, the paper demonstrates the practical implementation of STRATA through a representative problem. A comparison of the V&V concepts available in the Comprehensive Systems Design Language (CSDL), the natural language metamodel supporting STRATA, will be reviewed and compared to those provided in SysML v1 and v2. Interoperability of the languages will also be discussed.
Insights: Participants will glean the value of modeling methodologies and discern the distinctive advantages of STRATA. We will see how utilizing STRATA enhances modeling precision and efficiency, ultimately improving system design and development processes.