Evaluation of UML-RT and Papyrus-RT for Modelling Self-Adaptive Systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper is an evaluation of UML for Real-Time (UML-RT) for modelling Self-Adaptive Software (SAS) systems. Using a systematic review of the different features of UML-RT (optional capsules, SAP/SPP communication, hierarchical state machines, etc.), we analyse the suitability of the language for modelling structural and behavioural adaptations at design-and run-time. We evaluate these features in the context of their current state of support in Papyrus-RT, an Eclipse-based MDE tool for UML-RT recently developed by the Eclipse PolarSys Working Group. The use of UML-RT and Eclipse Papyrus for Real-Time (Papyrus-RT) for different kinds of adaptation is demonstrated using two real-time system case studies.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it