Towards automatic derivation of a product performance model from a UML software product line model
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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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 | 0.002 | 0.007 |
| 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 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".