A framework for software architecture refactoring using model transformations and semantic annotations
Bibliographic record
Abstract
Software-intensive systems evolve continuously under the pressure of new and changing requirements, generally leading to an increase in overall system complexity. In this respect, to improve quality and decrease complexity, software artifacts need to be restructured and refactored throughout their lifecycle. Since software architecture artifacts represent the highest level of implementation abstraction, and constitute the first step in mapping requirements to design, architecture refactorings can be considered as the first step in the quest of maintaining system quality during evolution. In this paper, we introduce an approach for refactoring software architecture artifacts using model transformations and quality improvement semantic annotations. First, the conceptual architecture view is represented as a UML 2.0 profile with corresponding stereotypes. Second, instantiated architecture models are annotated using elements of the refactoring context, including soft-goals, metrics, and constraints. Finally, the actions that are most suitable for the given refactoring context are applied after being selected from a set of possible refactorings. The approach is applied to a simple example, demonstrating refactoring transformations for improved maintainability, performance, and security
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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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".