Correct Reuse of Transformations is Hard to Guarantee
Bibliographic record
Abstract
As model transformations become more complex and more central to software development, reuse mechanisms become more important to enable effective and efficient development of high-quality transformations. A number of transformation-reuse mechanisms have been proposed, but so far there have been no effective attempts at evaluating the quality of reuse that can be achieved by these approaches. In this paper, we build on our earlier work on transformation intents and propose a systematic approach for analyzing the soundness and completeness of a given transformation reuse mechanism with respect to the preservation of transformation intent. We apply this approach to analyze transformation-reuse mechanisms currently proposed in the literature and show that these mechanisms are not sound or complete. We show why providing sound transformation reuse mechanisms is a hard problem, but provide some evidence that by limiting ourselves to specific families of transformations and modeling languages the problem can be simplified. As a result of our exploration, we propose a new research agenda into the development of sound (and possibly complete) transformation reuse mechanisms.
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How this classification was reachedexpand
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".