Automatic Generation of UML Diagrams From Product Requirements Described by Natural Language
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
In this paper, a novel approach is proposed to transform a requirement text described by natural language into two UML diagrams — use case and class diagrams. The transformation consists of two steps: from natural language to an intermediate graphic language called recursive object model (ROM) and from ROM to UML. The ROM diagram corresponding to a text includes the main semantic information implied in the text by modeling the relations between words in a text. Based on the semantics in the ROM diagram, a set of generation rules are proposed to generate UML diagrams from a ROM diagram. A software prototype R2U is presented as a proof of concept for this approach. A case study shows that the proposed approach is feasible. The proposed approach can be applied to requirements modeling in various engineering fields such as software engineering, automotive engineering, and aerospace engineering. The future work is pointed out at the end of this paper.
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