Combined propagation-based reasoning with goal and feature models
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 as well as analysis. Feature modeling, on the other hand, is a well-establishing technique for capturing commonalities and variabilities of Software Product Lines. When 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. Combined reasoning of goal and feature models is also fundamental to Concern-Driven Development, where concerns are composed not only based on functionality expressed with feature models, but also based on impact on stakeholder goals. Therefore, an analysis technique for feature and goal models based on a single conceptual model is desirable, because of its potential to streamline model analysis and reduce the complexity of the analysis framework. This paper introduces such a technique, i.e., a single, propagation-based reasoning algorithm that supports combined reasoning of goal and feature models and offers additional usability improvements over existing goal-oriented reasoning mechanisms.
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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 | 0.011 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".