XSLT transformation from UML models to LQN performance 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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| 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