Multi-Step Learning and Adaptive Search for Learning Complex Model Transformations from Examples
Bibliographic record
Abstract
Model-driven engineering promotes models as main development artifacts. As several models may be manipulated during the software-development life cycle, model transformations ensure their consistency by automating model generation and update tasks. However, writing model transformations requires much knowledge and effort that detract from their benefits. To address this issue, Model Transformation by Example (MTBE) aims to learn transformation programs from source and target model pairs supplied as examples. In this article, we tackle the fundamental issues that prevent the existing MTBE approaches from efficiently solving the problem of learning model transformations. We show that, when considering complex transformations, the search space is too large to be explored by naive search techniques. We propose an MTBE process to learn complex model transformations by considering three common requirements: element context and state dependencies and complex value derivation. Our process relies on two strategies to reduce the size of the search space and to better explore it, namely, multi-step learning and adaptive search. We experimentally evaluate our approach on seven model transformation problems. The learned transformation programs are able to produce perfect target models in three transformation cases, whereas precision and recall values larger than 90% are recorded for the four remaining cases.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".