Towards a DSM-based framework for the development of complex simulation systems
Bibliographic record
Abstract
Domain specific modelling, as a widely accepted software development paradigm in software engineering community, has attracted a lot of attention in M&S community for it raises the abstraction level and enables modelling with domain specific concepts. However, current DSM research in M&S community is not systematic and deep enough to provide generic support for simulation systems development, especially for complex simulation systems. To fulfill the full potential of DSM for M&S R&D, we need to combine the research fruits from both the M&S field and software engineering field. In this paper, We concentrate on using DSM for the development of complex simulation systems. Firstly We analyze the fundamental issues of applying DSM in complex M&S system development and list the obstacles which are not solved by current literature. Then domain-specific language (DSL) engineering research about the DSLs decomposition and composition in software engineering community are explicitly reviewed to gain insights, which enable us to propose a DSM-based framework for complex simulation systems development finally.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".