UML diagram synthesis techniques: a systematic mapping study
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
<b>Context:</b></br> \nThe Unified Modeling Language (UML), with its 14 different diagram types, is the de-facto standard modeling language for object-oriented modeling and documentation. Since \nthe various UML diagrams describe different aspects of one, and only one, software under \ndevelopment, they are not independent but strongly depend on each ot her in many ways. \nIn other words, diagrams must remain consistent. Dependencies between diagrams can become so intricate that it is sometimes even possible to synthesize one diagram on the basis of others. Support for synthesizing one UML diagram from other diagrams can provide the designer with significant help, thus speeding up the design process, decreasing the risk of errors, and guaranteeing consistency among the diagrams. \n \n<b>Objective:</b></br> \n \nThe aim of this article is to provide a comprehensive summary of UML synthesis techniques as they have been described in literature to date in order to obtain an extensive and \ndetailed overview of the current research in this area. \n \n<b>Method:</b></br> \nWe have performed a Systematic Mapping Study by following well-known guide-lines. We selected ten primary studies \nby means of a s
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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