Model-Based Systems Engineering Perspectives: A Survey of Practitioner Experiences and Challenges
Bibliographic record
Abstract
Model-based systems engineering (MBSE) is an established field, aiming to bring traceability and complexity management to the systems engineering process. However, multiple conceptual, technical, and organizational challenges continue to impede the effective deployment of MBSE in practice. This paper reports the results of a survey completed by 76 MBSE researchers and/or practitioners, on their organization’s use of MBSE. Our analysis indicates that many organizations have yet to fully leverage MBSE. Several have not completely transitioned to MBSE in their systems engineering processes, or do not adhere to any specific method, indicating a lack of a comprehensive and organization-wide MBSE approach. We find that challenges such as change management, cross-team collaboration, and tool customization persist. We report on these challenges and provide recommendations as potential solutions.
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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 | 0.046 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".