Bibliographic record
Abstract
UML metamodel, like other metamodel change through time as a result of changing needs and technical improvements during their life cycle.Adding new update or bug fixing can change UML metamodel, so potential inconsistencies with existing models that correspond to the previous version of the UML metamodel and may become non-compliant with the new version.In this approach, the refactoring facilitates a UML metamodel refactoring in well-defined steps from the basic features.The use of this refactoring allows extending the functionality of the existing UML metamodel.This research focuses on the methods and processes involved in adapting the UML metamodel to changing needs and technical improvements over time.The study highlights the potential for inconsistencies to arise from updates and bug fixing in the UML metamodel.The research methodology used is the refactoring of the UML metamodel through a well-defined process in well-defined steps.The study found that the refactoring process allows for the extension of the basic features of the UML metamodel and the introduction of new functionalities.The research concludes that the use of well-defined refactoring processes is essential in maintaining the evolution of the UML metamodel and ensuring its compliance with changing needs and technical improvements.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".