Increased system capability and heavier dependance on software has resulted in systems that are more complex than ever before. This complexity expansion impacts systems engineering analyses throughout the life-cycle of these systems, making system architecting more time consuming. Professional experience and exposure can play a role in the amount of time needed to understand and properly analyze a system, yet increased complexity also increases potential for human error in evaluation and interpretation. Failure to accurately capture system aspects can lead to either system failure or at the very least the accumulation of technical debt. The farther along the system is in its life-cycle, the more difficult it is to correct the issue. Therefore, the success of the system is dependent on the thorough understanding of the various components comprising the system architecture.  The authors will present a literature survey highlighting challenges associated with systems architecting and systems engineering for complex software systems-of-systems.

The paper will present the authors’ research and progress on an innovative application of Natural Language Processing (NLP) to aid the systems engineer, both in terms of comprehensiveness and effectiveness.   We are applying NLP to benefit Systems Engineers, specifically those working in the Model Based Systems Engineering (MBSE) domain. The authors evaluate common NLP techniques that can be applied to the highly technical and systematic written language methods used with MBSE.  Our paper will summarize contributions to the technical literature in this innovative application of NLP for MBSE. We will present a use case application study of the implementation of NLP to provide traceability between a proposed product architecture and a standard for compliance.