Guiding the Application of Design Patterns Based on UML Models
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
Software design patterns are documented best practice solutions that can be applied to recurring problems. Although well documented, there are often opportunities to apply them which are overlooked by software designers. This can be the result of inexperience, the sheer complexity of the system, or the fact that design patterns do not always constitute intuitive designs. In this paper, we present a structured methodology for semi-automating the detection of areas within a UML design of a software system that are good candidates for the use of design patterns. This is achieved by the definition of detection rules formalized using the OCL and using a decision tree model. The approach is illustrated on an example GoF design pattern. A prototype tool was developed to show the feasibility of the approach in practical situations, and is used on a case study, producing encouraging results
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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