Automating transition from use-cases to class model
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.
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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 | 0.011 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".