From UML to LQN by XML algebra-based model transformations
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
The change of focus from code to models promoted by OMG's Model Driven Development raises the need for verification of non-functional characteristics of UML models. such as performance, reliability, scalability, security, etc. Many modeling formalisms, techniques and tools have been developed over the years for the analysis of different non-functional characteristics. The challenge is not to reinvent new analysis methods for UML models, but to bridge the gap between UML-based software development tools and different kinds of existing analysis tools. Traditionally, the analysis models were built "by hand". However, a new trend is starting to emerge, that involves the automatic transformation of UML models (annotated with extra information) into various kinds of analysis models. This paper proposes a transformation method of an annotated UML model into a performance model. The mapping between the input model and the output model is defined at a higher level of abstraction based on graph transformation concepts, whereas the implementation of the transformation rules and algorithm uses lower-level XML trees manipulations techniques, such as XML algebra. The target performance model used as an example in this paper is the Layered Queueing Network (LQN); however, the transformation approach can be easily tailored to other performance modelling formalisms.
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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 | 0.001 |
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 it