UML diagram synthesis techniques: a systematic mapping study
Bibliographic record
Abstract
Context: \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 \nObjective: \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 \nMethod: \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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.035 | 0.084 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.023 | 0.015 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".