Evaluation of reusable concern-oriented goal models
Bibliographic record
Abstract
A new unit of encapsulation called the concern is at the center of Concern-Orientation. Building on techniques for advanced Separation of Concerns, from Model-Driven Engineering, and from Software Product Lines, Concern-Orientation is a reuse paradigm that stipulates the use of three interfaces to enable broad, generic reuse: the variation, customization, and usage interfaces. Higher-level concerns reuse lower-level concerns, resulting in concern hierarchies where lower-level concern models are composed with higher-level concern models. As part of the variation interface, goal models are used to describe the impact of features of a concern on system qualities. Consequently, goal models of lower-level concerns must be combined with goal models of higher-level concerns to enable reasoning about system qualities in concern hierarchies. However, existing propagation-based reasoning mechanisms for goal models still assume a monolithic goal model, which is not appropriate for concern-oriented reuse. To address this issue, this paper presents novel modeling constructs to enable the reuse of lower-level goal models in the context of Concern-Orientation, extends existing propagation-based reasoning mechanisms of goal models for use in concern hierarchies, and reports on a proof-of-concept implementation of the novel modeling constructs and the extended reasoning mechanism.
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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 | 0.009 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".