Improving design quality using meta‐pattern transformations: a metric‐based approach
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
Abstract Improving the design quality of large object‐oriented systems during maintenance and evolution is widely regarded as a high‐priority objective. Furthermore, for such systems that are subject to frequent modifications, detection and correction of design defects may easily become a very complex task that is even not tractable for manual handling. Therefore, the use of automatic or semi‐automatic detection and correction techniques and tools can assist reengineering activities. This paper proposes a framework whereby object‐oriented metrics can be used as indicators for automatically detecting situations for particular transformations to be applied in order to improve specific design quality characteristics. The process is based both on modeling the dependencies between design qualities and source code features, and on analyzing the impact that various transformations have on software metrics that quantify the design qualities being improved. Copyright © 2004 John Wiley & Sons, Ltd.
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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 | 0.017 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 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