On the role of design patterns in quality-driven re-engineering
Bibliographic record
Abstract
Design patterns have been widely adopted and well investigated by the software engineering community over the past decade. However, their primary use is still associated with forward engineering and the design phase of the software life-cycle. In this paper, we examine design patterns from a different perspective namely, their classification and usage for software re-engineering and restructuring. Specifically, twenty three design patterns originally presented in the "Gang of Four" book are reclassified for re-engineering purposes into two major categories, primitive and complex. Moreover, their relationships and impacts to specific re-engineering objectives are presented in terms of a layered model that is denoted by six different relations namely: uses, refines, conflicts, is-similar-to, combines-with, and requires. The paper also discusses how the classification scheme can be applied for the re-engineering and restructuring of object-oriented systems.
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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 | 0.024 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.006 | 0.015 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".