An Optimization Modeling Method for Adaptive Systems Based on Goal and Feature Models
Bibliographic record
Abstract
Adaptive Socio-Cyber-Physical Systems (SCPSs) need a comprehensive requirements modeling approach to embed social concerns (goals) in their development activities. Since these kinds of systems often involve complicated and dynamic interactions with their environment, they must react to environmental changes using different potential solutions that satisfy social concerns as well as system objectives and qualities. This paper presents an optimization modeling method that monitors an SCPS's environment and qualities to provide design-time and runtime solutions that satisfy required goals of the system and its stakeholders, as well as imposed correctness constraints specified in a feature model. We combine arithmetic functions generated automatically from goal and feature models as an objective function input to an optimization tool (IBM CPLEX) in order to compute, at design time, optimal solutions for common situations. Runtime optimization can also be used for unforeseen situations. An illustrative example is used to assess the feasibility of the method. The results show that optimizing the mathematical functions of goal/feature models together is beneficial in exploring SCPS requirements and detecting weaknesses in common adaptation situations.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".