Enabling Design-Space Exploration for Domain-Specific Modelling
Bibliographic record
Abstract
Design-Space Exploration (DSE) looks for a suitable candidate solution to a problem, with respect to a set of criteria, by searching through a space of possible solution designs. Domain-Specific Modelling (DSM) allows language engineers to create Domain-Specific Languages (DSLs) for a particular domain, allowing non-technical domain experts to use the DSL to model a system, analyse, optimise or transform the model, generate code or documentation, etc. This paper presents a framework to enable DSE for DSM, so that non-technical domain experts can define DSE input using DSL syntax, and obtain DSL instances as a result of execution the DSE. The contribution of our framework is twofold: (1) automatic generation of a family of related DSLs (to describe structural constraints as well as constraints on simulation results) for modelling a DSE problem at the DSL level from a given DSL definition, and (2) generic support for executing a DSE algorithm, which searches the design space and generates suitable DSL instances. The framework can be applied to any explicitly defined DSL with an explicitly defined semantic domain. We evaluate this claim by applying our framework to a user-defined Simulink library. The approach is explained using a DSL for modelling electronic filters.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".