Designing DEVS visual interfaces for end-user programmers
Bibliographic record
Abstract
Although the Discrete Event System specification (DEVS) has over recent decades provided systems engineers with a scalable approach to modeling and simulation, the formalism has seen little uptake in many other disciplines where it could be equally useful. Our observations of end-user programmers confronted with DEVS theory or software suggest that learning barriers are largely responsible for this lack of utilization. To address these barriers, we apply ideas from human–computer interaction to the design of visual interfaces intended to promote their users’ effective knowledge of essential DEVS concepts. The first step is to propose a set of names that make these concepts easier to learn. We then design and provide rationale for visual interfaces for interacting with various elements of DEVS models and simulation runs. Both the names and interface designs are evaluated using the Cognitive Dimensions of Notations framework, which emphasizes trade-offs between 14 aspects of information artifacts. As a whole, this work illustrates a generally applicable design process for the development of interactive formalism-based simulation environments that are learnable and usable to those who are not experts in simulation formalisms.
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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 | 0.012 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".