Towards automatic derivation of a product performance model from a UML software product line model
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
Software Product Line (SPL) engineering is a software development approach that takes advantage of the commonality and variability between products from a family, and supports the generation of specific products by reusing a set of core family assets. This paper proposes a UML model transformation approach for software product lines to derive a performance model for a specific product. The input to the proposed technique, the "source model", is a UML model of a SPL with performance annotations, which uses two separate profiles: a "product line" profile from literature for specifying the commonality and variability between products, and the MARTE profile recently standardized by OMG for performance annotations. The source model is generic and therefore its performance annotations must be parameterized. The proposed derivation of a performance model for a concrete product requires two steps: a) the transformation of a SPL model to a UML model with performance annotations for a given product, and b) the transformation of the outcome of the first step into a performance model. This paper focuses on the first step, whereas the second step will use the PUMA transformation approach of annotated UML models to performance models, developed in previous work. The output of the first step, named "target model", is a UML model with MARTE annotations, where the variability expressed in the SPL model has been analyzed and bound to a specific product, and the generic performance annotations have been bound to concrete values for the product. The proposed technique is illustrated with an e-commerce case study.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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