Arithmetic Semantics of Feature and Goal Models for Adaptive Cyber-Physical Systems
Bibliographic record
Abstract
Many Cyber-Physical Systems (CPSs) today are self-adaptive, in order to handle frequent changes in environmental conditions and requirements. In CPSs, goal-based reasoning is often used to include stakeholder and social concerns in decision making during design and runtime adaptation activities. To better support some of these activities, arithmetic semantics for goal models were proposed to enable the generation of mathematical functions usable by systems. However, goal models often allow invalid combinations of alternatives, which can be prevented by companion feature models. In this paper, to enable the generation of valid and optimal configurations for adaptive CPSs, we propose new arithmetic semantics for feature models that enable their transformations to mathematical functions (in several programming languages) further restricting the ones generated from goal models. The composition of feature and goal functions results in a smaller design space, leading to fewer but valid solutions that can be generated (e.g., through optimization) and used in simulations and running adaptive CPSs with social concerns. Finally, a simulation model in SysML is proposed in this paper to demonstrate the feasibility and usefulness of this composition.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".