Towards Checking Consistency-Breaking Updates between Models and Generated Artifacts
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-based Low-Code systems rely on high-level specifications (models) to generate all artifacts of the resulting software. Such artifacts can be code, schemas, as well as data, and metadata. Maintaining consistency between models and artifacts generated from them is at the core of generative approaches in software engineering. Existing approaches have focused on the consistency problem between specific pairs of artifacts, such as models and their metamodels, class diagrams and generated code, and database schemas and data. Instead, we envision a holistic approach for maintaining the consistency that encompasses all generated artifacts. In this paper, we motivate our approach with a case study from a real model-driven software system. We identify scenarios where updates to either models or generated artifacts break consistency and outline a set of challenges and future research directions.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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