Higher-Order Transformation for Incremental Propagation of Changes from Software to Performance Models
Bibliographic record
Abstract
This paper proposes a higher-order transformation (HOT) for realizing Incremental Change Propagation (ICP) from software UML models extended with performance annotations to performance Layered Queueing Network (LQN) models. Such a transformation is necessary for integrating quantitative performance analysis into the model-driven engineering of real-time systems. The entire process starts by automatically generating an LQN and a trace model from a UML model extended with MARTE annotations, with a batch Epsilon ETL transformation previously developed by the authors. The textual ETL transformation definition is translated to an ETL transformation model using the Epsilon Haetae tool. The ETL transformation model conforms to the ETL metamodel and represents the mapping between source and target models at a high level of abstraction. We use it to answer the question: what needs to be changed in the target model upon detecting changes in the source model? During the development process, when the UML model evolves, we detect such changes with the Eclipse EMF Compare tool, then incrementally propagate them to the LQN model to keep it synchronized. The extended approach is illustrated by applying it to an e-commerce model from the literature. The execution time of ICP is measured and compared to the traditional batch transformation.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".