Bibliographic record
Abstract
The creation of a simulation model, like the creation of any software product, is guided by principles and procedures that have been reasonably well established within the software engineering community. In the context of a simulation, we need to be able to characterize the dynamic behavior of the system, and such characterization should be expressed in a format that is as clear and unambiguous as possible. Wherever feasible, formal approaches should be used. One of these formal techniques, the DEVS formalism, has gained popularity in recent years. Although some efforts have been dedicated to the Validation and Verification (V&V) of DEVS models, this is an open research area with interesting opportunities for application of advanced software engineering techniques. Indeed, it appears that thanks to the characteristics of DEVS models and the fact that DEVS models can be executed (e.g., the CD++ toolkit allows the use of DEVS and Cell-DEVS formalisms) well-known software testing techniques are worth investigating for the V&V of DEVS models. In this article, we show these similarities and discuss open research paths in the field of DEVS modeling Verification and Validation by means of testing.
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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 | 0.014 | 0.042 |
| 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 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| 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".