Designing UML and UML-based diagrams for technical documentation
Bibliographic record
Abstract
UML diagrams are used to model real-world ideas and help users understand complex programming concepts. Developers and writers need to produce well-formed UML diagrams that can convey these ideas, and that are suitable for publishing in technical documentation. This paper examines the evolution of UML diagrams and tooling, with a focus on practices at the IBM Toronto Software Laboratory. It reviews the findings of two previous papers, which described obstacles to creating UML diagrams for publication and outlined numerous steps to help developers, writers, and graphic designers create useful UML diagrams. It shows how developers at the IBM Toronto Software Laboratory have added new features to existing modeling programs to improve the usability and design functions in IBM's suite of modeling tools. It describes the relative strengths and weaknesses of the two main types of graphics, and illustrates the beneficial impact of the addition of the SVG graphic export function to IBM's tooling. It shows how these functional improvements have resulted in a higher quality of UML diagrams submitted for publication by both technical and non-technical users.
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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 | 0.029 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".