Product Model Derivation by Model Transformation in Software Product Lines
Bibliographic record
Abstract
Product derivation is an essential part of the Software Product Line (SPL) development process. The paperproposes a model transformation for deriving automatically a UML model of a specific product from the UML model of a product line. This work is a part of a larger project aiming to integrate performance analysis in the SPL model-driven development. The SPL source model is expressed in UML extended with 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 automatic derivation of a concrete product model based on a given feature configuration is enabled through the mapping between features from the feature model and their realizations in the design model. The paper proposes an efficient mapping technique that aims to minimize the amount of explicit feature annotations in the UML design model of SPL. Implicit feature mapping is inferred during product derivation from the relationships between annotated and non-annotated model elements as defined in the UML metamodel and well formedness rules. The transformation is realized in the Atlas Transformation Language (ATL) and illustrated with an ecommerce case study that models structural and behavioural SPL views.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".