Visualizing 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
Many modern systems are built to be in use for a long time, sometimes spanning several decades. Furthermore, considering the cost of replacing an existing system, existing systems are usually adapted to evolving requirements, some of which may be anticipated. Such systems need to be specified and analyzed in terms of whether the changes introduced into the system still address evolving requirements and continue to satisfy the needs of its stakeholders. Recently, the User Requirements Notation (URN) - a requirements engineering notation standardized by the International Telecommunication Union for the elicitation, specification, and analysis of integrated goal and scenario models - has been extended with support for the modeling and analysis of evolving requirements models by capturing a comprehensive set of changes to a URN model as first-class model concepts in URN. The extension is called TimedURN and this paper builds on TimedURN to analyze and visualize not just an individual time point in the past, present, or future but a time range consisting of a set of time points. Various visualization options are discussed, including their proof-of-concept implementation in the jUCMNav requirements engineering tool.
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 | 0.001 |
| 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