Bibliographic record
Abstract
Emerging socio-cyber-physical systems integrate social concerns, often captured with goal models, with complex systems, where structure and behavior are often captured in SysML. Traceability between these two types of models is important to reason about consistency, completeness, and the impact of modifications. However, managing traceability during the co-evolution of these two views is not well supported as SysML does not provide sophisticated goal-modeling capabilities out of the box. This paper proposes an approach where the Goal-oriented Requirement Language (GRL) is used to capture and analyze social concerns as a supplement to SysML models, and where traceability is handled via a third-party requirements management system, namely IBM Rational DOORS. The approach is supported with tools automating the import in DOORS of relevant parts of the GRL and SysML models from their respective modeling environments (jUCMNav and No Magic's Cameo Systems Modeler). A traceability information model is proposed to connect elements from GRL and SysML models in a way that enables automating important completeness and consistency checks, even as the models evolve. The approach is illustrated and evaluated with a Smart Home example, with a discussion of benefits and limitations.
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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 | 0.020 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".