An MBSE Architectural Framework for the Agile Definition of Complex System Architectures
Bibliographic record
Abstract
In the recent years, a shift from document-based to model-based approaches is going on within many organizations and industries involved in the development of complex systems. Model Based Systems Engineering (MBSE) methods and tools are in fact gaining more and more popularity due to all their claimed benefits over traditional document-based approaches, including for instance enhanced design quality of systems, clearer development of system requirements and specifications and improved communications within the design teams. However, these benefits can be possible only if recommendations on how generating and representing design information during the development process are made available. The present paper introduces a new model-based <em>architectural framework</em>, i.e. a guideline that leverages a modeling approach for the development and representation of complex systems. More specifically, the MBSE architectural framework addressed in this paper focuses on the system architecting activities of a Systems Engineering Product Development process, i.e. when multiple conventional and innovative solutions of the systems are generated to address all the system stakeholder expectations. The proposed architectural framework aims at fostering the <em>agility </em>of the development of complex systems, in order to streamline, improve and accelerate their architectures definition and modeling through an MBSE approach. The paper provides details of the MBSE architectural framework, including the means to produce and represent all the system development information. 10.2514/6.2022-3720
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".