Combined goal and feature model reasoning with the User Requirements Notation and jUCMNav
Bibliographic record
Abstract
The User Requirements Notation (URN) is an international requirements engineering standard published by the International Telecommunication Union. URN supports goal-oriented and scenario-based modeling and analysis. jUCMNav is an open-source, Eclipse-based modeling tool for URN. This tool demonstration focuses on recent extensions to jUCMNav that have incorporated feature models into a URN-based modeling and reasoning framework. Feature modeling is a well-establishing technique for capturing commonalities and variabilities of Software Product Lines. Combined with URN, it is possible to reason about the impact of feature configurations on stakeholder goals and system qualities, thus helping to identify the most appropriate features for a stakeholder. Furthermore, coordinated feature and goal model reasoning is fundamental to Concern-Driven Development, where concerns are defined with a three-part variation, customization, and usage interface. As the variation interface is described with feature and goal models, it is now possible with jUCMNav to define and reason about a concern's variation interface, which is a prerequisite for composing multiple concerns based on their three-part interfaces.
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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 | 0.014 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".