Specifying evolving requirements models with TimedURN
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 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.002 |
| Open science | 0.001 | 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