Non-Deterministic Use Case Map Traversal Algorithm for Scenario Simulation and Debugging
Bibliographic record
Abstract
The User Requirements Notation (URN) is a Requirements Engineering modeling language published by the International Telecommunication Union (ITU) to formally specify and analyze what a user would expect from a system. In particular, URN allows the modeling of use cases and scenarios of a system with Use Case Maps (UCM). A key benefit of formalizing these models is the added ability to better analyze them; thus gaining insight to improve quality and understanding of the requirements of the system and its capabilities. Existing traversal mechanisms which analyze UCM do not well reflect the inherent stochasticity of system or user interactions, because they are typically designed for visualization purposes rather than simulation and debugging. We propose a novel traversal mechanism that (i) better reflects real systems by incorporating non-determinism, (ii) considers multiple independent scenarios running concurrently, (iii) implements the UCM concept of map instances, and (iv) consequently enables automated simulation and execution as well as user-driven forward and backward debugging of UCM. We validate the novel traversal mechanism by applying it to a crisis response mobile app that allows a first responder to step forwards and backwards through crisis response actions.
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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 | 0.003 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".