XSLT transformation from UML models to LQN performance models
Bibliographic record
Abstract
A graph grammar-based transformation of a UML design model into a Layered Queueing Network (LQN) performance model was previously proposed by the authors of this paper. The actual transformation was implemented in two ways: first by using an existing graph-rewriting tool, and secondly through an ad-hoc graph transformation implemented in Java.This paper extends the previous work of the authors by proposing a third approach to implement the UML to LQN transformation by using XSLT. Recommended by the World Wide Web Consortium (W3C) the Extensible Stylesheet Language for Transformations (XSLT) is a flexible language for transforming XML documents into various formats including HTML, XML, text, PDF, etc. The input to our XSLT transformation is an XML file that contains the UML model in XML format according to the standard XML Metadata Interchange (XMI). The output is the corresponding LQN model description file, which can be read directly by existing LQN solvers. The paper compares the relative advantages and disadvantages of the XSLT transformation with the previous approaches proposed by the authors, describes the principles of the XSLT transformation and applies it to a case study.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.005 | 0.002 |
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".