Domain-specific engineering of domain-specific languages
Bibliographic record
Abstract
Domain-specific modelling (DSM) enables experts of arbitrary domains to perform modelling tasks using familiar constructs. This contrasts with common code-centric development approaches where programmers deal with object-oriented approximations of higher level concepts. Domain-specific concepts and their relationships are captured by domain-specific languages (DSLs). Unfortunately, it is common practice for DSLs to be specified within the object-oriented mindsets of classes and associations. This approach not only contradicts the model-driven engineering (MDE) philosophy of development using domain-specific concepts -- in this case, the domain and concepts of DSLs --, it is also faced with the same obstacle as past UML-to-code generation efforts; namely, that UML models are too generic to enable complete program synthesis. In the context of DSL engineering, this obstacle translates to the necessity for DSL designers to explicitly define DSL semantics manually (e.g., via coded generators and/or model transformations). In this work, we propose a novel approach to DSL design where low level modelling formalisms are seamlessly woven together to form new DSLs whose semantics are fully automatically generated.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".