Automating transition from use-cases to class model
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
To identify objects from the requirements and to model the problem in classes are critical in object-oriented analysis and design (OOAD). Unfortunately, this is recognized as a hard task for most software engineers, because both domain experience and expertise are needed, since there is no crisp guideline. We present an approach with a set of artifacts and methodologies, and to automate the transition from requirement to detail design. Use cases are applied as the method to capture and record requirements. All the use cases are formalized by a use case template. A glossary that contains the domain vocabulary is used throughout the OOAD process to reduce the vagueness of natural language. Some language patterns are introduced to make the automatic processing of use cases possible. We apply robustness analysis to bridge the gap between a use case and its realization, i. e. between a use case and the corresponding collaboration diagram in UML. Some rules are summarized and adopted to automate the object/class identification and behavior distribution among the classes. The implementation of the tool is described.
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 | 0.001 |
| 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