Towards a body of knowledge for model-based software engineering
Bibliographic record
Abstract
Model-based Software Engineering (MBSE) is now accepted as a Software Engineering (SE) discipline and is being taught as part of more general SE curricula. However, an agreed core of concepts, mechanisms and practices --- which constitutes the Body of Knowledge of a discipline --- has not been captured anywhere, and is only partially covered by the SE Body of Knowledge (SWEBOK). With the goals of characterizing the contents of the MBSE discipline, promoting a consistent view of it worldwide, clarifying its scope with regard to other SE disciplines, and defining a foundation for a curriculum development on MBSE, this paper provides a proposal for an extension of the contents of SWEBOK with the set of fundamental concepts, terms and mechanisms that should constitute the MBSE Body of Knowledge.
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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 | 0.026 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.014 | 0.027 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".