Designing UML and UML-based diagrams for technical documentation
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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