Algorithmic support for model transformation in object‐oriented software development
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Current methods for object‐oriented software development provide notation for the specification of models, yet do not sufficiently relate the different model types to each other, nor do they provide support for transformations from one model type to another. This makes transformations a manual activity, which increases the risk of inconsistencies among models and may lead to a loss of information. We have developed and implemented an algorithm supporting one of the transitions from analysis to design, the transformation of scenario models into behavior models. This algorithm supports the Unified Modelling Language (UML), mapping the UML's collaboration diagrams into state transition diagrams. We believe that CASE tools implementing such algorithms will be highly beneficial in object‐oriented software development. In this paper, we provide an overview of our algorithm and discuss all its major steps. The algorithm is detailed in semi‐formal English and illustrated with a number of examples. Furthermore, the algorithm is assessed from different perspectives, such as scope and role in the overall development process, issues in the design of the algorithm, complexity, implementation and experimentation, and related work. Copyright © 2001 John Wiley & Sons, Ltd.
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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 | 0.001 |
| 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 | 0.002 |
| 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