PMExec: An Execution Engine of Partial UML-RT Models
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 presents PMExec, a tool that supports the execution of partial UML-RT models. To this end, the tool implements the following steps: static analysis, automatic refinement, and input-driven execution. The static analysis that respects the execution semantics of UML-RT models is used to detect problematic model elements, i.e., elements that cause problems during execution due to the partiality. Then, the models are refined automatically using model transformation techniques, which mostly add decision points where missing information can be supplied. Third, the refined models are executed, and when the execution reaches the decision points, input required to continue the execution is obtained either interactively or from a script that captures how to deal with partial elements. We have evaluated PMExec using several use-cases that show that the static analysis, refinement, and application of user input can be carried out with reasonable performance, and that the overhead of approach is manageable. https://youtu.be/BRKsselcMnc Note: Interested readers can refer to [1] for a thorough discussion and evaluation of this work.
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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 | 0.000 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 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