Frequently Asked Questions

• Document-Based Systems Engineering (DBSE) is well known as a classic case where life cycle activities generate documents as artifacts.
• Model-Based Systems Engineering (MBSE) generates, instead, a set of model elements with relationships forming a system model.

Model-Based Systems Engineering (MBSE) as defined by the International Council on Systems Engineering (INCOSE) is “the formalized application of modelling to support system requirements, design, analysis, verification and validation activities beginning in the conceptual design phase and continuing throughout development and later life cycle phases.”

The term MBSE comprises multiple modeling concepts: modeling language, modelling method, and modeling tools in order to produce one system model or more. A system model contains model elements (e.g., requirements, use cases, functions, logical components…) and relationships in between (e.g., re‐ fine, allocate, derive…).

Indeed, the MBSE approach does not necessarily change the “what to do” by systems engineers, instead changes the “how to do it”. Particularly, MBSE goes beyond the DBSE approach by considering the use of system models instead of documents as the primary artifacts produced during the life cycle activities. Moreover, such models are specified, reviewed, and released using a systems modelling tool (following a modelling language such as SysML) and not just a drawing or documentation tool as Visio, PowerPoint or Excel.

Our SysDICE team is specialized in the adoption of MBSE across all its phases including competency development of the involved team members, method development for the intended MBSE applications, and MBSE tools customization to achieve a valuable MBSE adoption.

Sed ut perspiciatis unde omnis iste natus error sit voluptatem accusantium doloremque laudantium, totam rem aperiam, eaque ipsa quae ab illo inventore veritatis et quasi architecto beatae vitae dicta sunt explicabo. Nemo enim ipsam voluptatem quia voluptas sit aspernatur aut odit aut fugit, sed quia consequuntur magni dolores eos qui ratione voluptatem sequi nesciunt.

Neque porro quisquam est, qui dolorem ipsum quia dolor sit amet, consectetur, adipisci velit, sed quia non numquam eius modi tempora incidunt ut labore et dolore magnam aliquam quaerat voluptatem. Ut enim ad minima veniam, quis nostrum exercitationem ullam corporis suscipit laboriosam, nisi ut aliquid ex ea commodi consequatur?

Quis autem vel eum iure reprehenderit qui in ea voluptate velit esse quam nihil molestiae consequatur, vel illum qui dolorem eum fugiat quo voluptas nulla pariatur?

Based on our MBSE research and industrial work, we explored the topic of MBSE adoption challenges and found out the importance of alignment among MBSE community members for effective understanding of the actual status of MBSE adoption challenges. Therefore, in early 2017, we decided to identify a set of MBSE adoption challenges and then ask, through an online survey, participants to collect the opinion concerning these challenges, what phase of MBSE adoption they occur in, and the dependencies between them. We presented the publication described in this post at the EMEA Sector Systems Engineering Conference 2018.

The results of this survey work demonstrate how the survey objectives have been accomplished, they open the opportunity to overcome MBSE adoption challenges and thus to achieve effective MBSE implementation. We believe that the challenges faced are not only due to MBSE but the way it is adopted. A long-term vision should not only consider the tools aspect but also target and solve MBSE methodology, technological, educational, MBSE training and human factor perspectives. This is part of our SysDICE consultancy services where we help our customers to optimize their MBSE journey.

Sed ut perspiciatis unde omnis iste natus error sit voluptatem accusantium doloremque laudantium, totam rem aperiam, eaque ipsa quae ab illo inventore veritatis et quasi architecto beatae vitae dicta sunt explicabo. Nemo enim ipsam voluptatem quia voluptas sit aspernatur aut odit aut fugit, sed quia consequuntur magni dolores eos qui ratione voluptatem sequi nesciunt.

Neque porro quisquam est, qui dolorem ipsum quia dolor sit amet, consectetur, adipisci velit, sed quia non numquam eius modi tempora incidunt ut labore et dolore magnam aliquam quaerat voluptatem. Ut enim ad minima veniam, quis nostrum exercitationem ullam corporis suscipit laboriosam, nisi ut aliquid ex ea commodi consequatur?

Quis autem vel eum iure reprehenderit qui in ea voluptate velit esse quam nihil molestiae consequatur, vel illum qui dolorem eum fugiat quo voluptas nulla pariatur?

In addition to rule-based techniques derived from the field of computational linguistics, NLP can use both Machine Learning (ML) and Deep Learning (DL) methodologies in order to effectively take and process unstructured text and speech datasets, thus overlapping with AI.