Generating Examples for Knowledge Abstraction in MDE: a Multi-Objective Framework.
Bibliographic record
Abstract
Model-Driven Engineering (MDE) aims at raising the level of abstraction in software development and therefore relies on task automation. To foster automation, MDE promotes the use of specific domain languages (DSLs), essential to express ideas at the domain level. Furthermore, to ease communication between computer science and other fields, modelers employ model examples (i.e., selected metamodel instances) to illustrate and refine their conceptual ideas. But, if the use of model examples has shown its efficiency, it is still an ad hoc process which requires automation. In this paper, we briefly depict the thorough example-toknowledge learning process. Then, we present a framework that produces, from a metamodel, a representative model example set with regards to a given coverage definition. To find the best trade-off between coverage and a necessary minimality objectives, we use a non-dominated genetic algorithm (NSGAII). We illustrated our method by generating a near-optimal set of models for the peculiar constraint learning task. We evaluated its efficiency comparing the resulting generated set with the best one issued from a raw random generation. Our encouraging preliminary results let us envision a deep study of the relation between various types of coverage and their impact on our ability to abstract knowledge from examples.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Model-driven software engineering method for generating model examples; a software technique, not a study of research.
The paper develops an automated model-generation framework for software engineering, not a study of research itself.
Software-engineering method for generating MDE model examples; SE tooling, not study of how research is done.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".