Bibliographic record
Abstract
Modelling languages often lack explicit support for reuse, and there are very few libraries of reusable models available to developers.This is especially true for use cases, one of the most wide-spread modelling languages used to describe systems at a high level of abstraction during requirements elicitation.This paper proposes Concern-Oriented Use Cases (CoUC), a use case modelling language designed to support planned and opportunistic reuse.CoUC makes it possible to create libraries of generic recurring interaction scenarios, provides means to modularize crosscutting interaction patterns and supports feature-oriented scenario extensions.We provide a metamodel that defines the hierarchical structure and behavioural scenario descriptions for use cases.We further elaborate a use case composition algorithm capable of combining the reusing and reused use cases.To validate our approach, the CoUC language and composition algorithm have been implemented in the TouchCORE modelling tool, and applied to model three examples which showcase feature-oriented use case extension, reuse of a generic use case, as well as software product line development and evolution.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".