From UML to LQN by XML algebra-based model transformations
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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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".