Automatic Derivation of Performance Models in the Context of Model-Driven SOA
Why this work is in the frame
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Bibliographic record
Abstract
The thesis proposes a model transformation chain called Performance from Unified Modeling Analysis for Service-Oriented Architecture (SOA) systems (PUMA4SOA), whose purpose is to automatically generate performance models from the UML software design models of SOA systems with performance annotations. The main goal of PUMA4SOA is to enable the analysis of performance properties of software systems in the early software development phases, which helps developing SOA systems that meet their performance requirements. PUMA4SOA extends PUMA, an existing transformation approach from software to performance models developed in our research group. The main differences between PUMA4SOA and PUMA are as follows: a) focus on SOA systems; b) application of Model-Driven Architecture (MDA) principles of considering first software platform-independent models (PIM) which are then transformed into platform-specific models (PSM); c) use of a Platform Completion (PC) feature model to define variability of platform characteristics; d) use of aspect-oriented modeling (AOM) techniques to specify realization of platform features; and e) systematic use of trace-links between different types of models (i.e., software, intermediate and performance models). PUMA4SOA accepts the following input models: the software platform independent model, the deployment model, the PC-feature models and a set of platform aspect models. Similar to PUMA, PUMA4SOA makes use of an intermediate model called Core Scenario Model (CSM). The model transformations chain of PUMA4SOA begins by transforming the UML PIM to a CSM PIM, which in turn is used to generate a CSM PSM using an AOM approach. The third model transformation maps the CSM platform specific model into a performance model (Layered Queuing Network in this case)
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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 | 0.001 | 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 | 0.000 |
| 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