Automated Patch Generation for Fixing Semantic Errors in ATL Transformation Rules
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
With the growing popularity of the MDE paradigm, model transformations are becoming more and more complex. ATL transformations, in particular, are error-prone due to the declarative nature of the language and the dependency towards the involved metamodels. To alleviate the burden of developers, we propose, in this paper, an approach for fixing semantic errors in ATL transformation rules without predefined patch templates for specific error types. In a first step, our approach determines the rules that are likely to contain errors starting from the discrepancy between the expected and produced outputs of test cases. Then, a second step allows to generate candidate patches for these errors using a multiobjective optimization algorithm, guided by the same test cases. In a preliminary evaluation, we show that our approach can fix most of the errors for transformations with one or two errors. For those with multiple errors, more iterations are necessary to fix some of the errors.
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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