Fighting evil is not enough when refactoring metamodels
Bibliographic record
Abstract
In model-driven engineering, metamodels are central artifacts that allow to capture domain concepts and build domain-specific languages. However, bad design decisions, continuous changes, and the evolution of requirements may introduce bad smells and deteriorate the quality of metamodels. Refactoring metamodels is a complex task as it should be performed according to many conflicting quality attributes while maximizing the removal of smells. In this paper, we propose a generic automated approach based on a multi-objective heuristic search to refactor metamodels. The process aims at generating a set of refactoring recommendations with various quality trade-offs from which the modeler can choose the most appropriate for her context. We evaluate the efficiency of our approach with a user-based experiment, on time to perform understandability and extendibility tasks, as well as the correctness of the task output. Our results show that, globally, considering trade-offs between quality and smell removal is significantly better than focusing on smell removal alone. The observed difference is statistically significant for the extendibility but only partially for the understandability.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.007 | 0.008 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".