Using Aspects for Platform-Independent to Platform-Dependent 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
This paper presents an aspect-based approach for realizing a transformation from platform-independent to platform-dependent models in the context of a model transformation chain that generates queueing-based performance models from UML design models of serviceoriented applications. The purpose of generating such performance models is to evaluate the performance of the system under development in the early software lifecycle phases, in order to insure that it will meet the performance requirements. The paper presents the model transformation chain PUMA4SOA, which transforms automatically a UML model of a service-oriented architecture (SOA) system extended with MARTE performance annotations into an intermediate model, Core Scenario Model (CSM), which in turn is used to generate a Layered Queueing Network (LQN) performance model. Aspect-oriented modeling is used to represent different services offered by the underlying SOA platform to the SOA application. The paper discusses and compares different alternatives for composing the platform aspect models with the platform-independent model (PIM) of the application throughout the model transformation chain.
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 | 0.001 |
| 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