Partial evaluation of model transformations
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
Model Transformation is considered an important enabling factor for Model Driven Development. Transformations can be applied not only for the generation of new models from existing ones, but also for the consistent co-evolution of software artifacts that pertain to various phases of software lifecycle such as requirement models, design documents and source code. Furthermore, it is often common in practical scenarios to apply such transformations repeatedly and frequently; an activity that can take a significant amount of time and resources, especially when the affected models are complex and highly interdependent. In this paper, we discuss a novel approach for deriving incremental model transformations by the partial evaluation of original model transformation programs. Partial evaluation involves pre-computing parts of the transformation program based on known model dependencies and the type of the applied model change. Such pre-evaluation allows for significant reduction of transformation time in large and complex model repositories. To evaluate the approach, we have implemented QvtMix, a prototype partial evaluator for the Query, View and Transformation Operational Mappings (QVT-OM) language. The experiments indicate that the proposed technique can be used for significantly improving the performance of repetitive applications of model transformations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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