Fully verifying transformation contracts for declarative ATL
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
The Atlas Transformation Language (ATL) is today a de-facto standard in model-driven development. It is understood by the community that methods for exhaustively verifying such transformations provide an important pillar for achieving a stronger adoption of model-driven development in industry. In this paper we propose a method for verifying ATL model transformations by translating them into DSLTrans, a transformation language with limited expressiveness. Pre-/postcondition contracts are then verified on the resulting DSLTrans specification using a symbolic-execution property prover. The technique we present in this paper is exhaustive for the declarative ATL subset, meaning that if a contract holds, it will hold when any input model is passed to the ATL transformation being checked. We explore the scalability of our technique using a set of examples, including a model transformation developed in collaboration with our industrial partner.
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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 | 0.000 | 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 | 0.000 | 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