Generating scenarios from use case map specifications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The use case map (UCM) notation is being standardized as part of the user requirements notation (URN), the most recent addition to ITU-T's family of languages. UCM models describe functional requirements and high-level designs with causal paths superimposed on a structure of components. The generation of individual scenarios from UCM specifications enables the validation of requirements and facilitates the transition from requirements to design. In this paper, we address the challenges faced during the automated generation of such scenarios. Scenario definitions and traversal algorithms are first used to extract individual scenarios from UCMs and to store them as XML files. Transformations to other scenario languages (for instance, message sequence charts) are then achieved using XSLT. Possible applications of this two-step generation process include early validation and synthesis of design models. Illustrative examples are given based on our current tools and recent experiments.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it