Using Constraints with Action Language for Model Evolution.
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
Abstract- Since the advent of model driven software engineering (MDSE) it has become necessary to develop techniques and tools for model evolution. In this paper we examine two issues and propose a solution to resolve them. The first is the automation of model evolution and the second is the support of software evolution in modeling languages. We extend Object Constraint Language (OCL) with actions and define a new language CAL (Constraints with Action Language), which gives a user the ability to use constraints with actions on models. CAL contains a small set of constructs, but is powerful enough to be used efficiently for typical software evolution management operations like impact analysis, correction, improvement and enhancement of models. One of the CAL applications, a prototype tool VCAL (visual CAL), for dependency analysis of UML Class Diagrams is presented.
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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 | 0.000 |
| 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