Specifying Evolving Requirements Models with TimedURN
Bibliographic record
Abstract
The User Requirements Notation (URN) supports the elicitation, specification, and analysis of integrated goal and scenario models. The analysis of the goal and scenario models focuses on one snapshot in time and does not allow the model to change over time. While several models may be created that represent different stages of a system, managing several, slightly different model copies is a space-consuming, time-consuming, and error-prone task that makes it difficult to maintain consistency across the model copies. This paper introduces TimedURN, an extension of the URN standard, which enables the modeling and analysis of a comprehensive set of changes to a goal and scenario model over time. The changes to the model are captured in one base model, which eases system evolution. The metamodel for TimedURN is presented and it is argued that it can also be applied to other modeling languages. Furthermore, the usefulness of TimedURN is illustrated with an example from the sustainability domain and the comprehensiveness of the supported types of changes is assessed.
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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 | 0.012 |
| 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 | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".