Toward Intelligent Generation of Tailored Graphical Concrete Syntax
Bibliographic record
Abstract
In model-driven engineering, the concrete syntax of a domain-specific modeling language (DSML) is fundamental as it constitutes the primary point of interaction between the user and the DSML. Nevertheless, the conventional one-size-fits-all approach to concrete syntax often undermines the effectiveness of DSMLs, as it fails to accommodate the diverse constraints and specific requirements inherent to diverse users and usage contexts. Such shortcomings can lead to a significant decline in the performance, usability, and efficiency of DSMLs. This vision paper proposes a conceptual framework to generate concrete syntax intelligently. Our framework considers multiple concerns of users and aims to align the concrete syntax with the context of the DSML usage. Additionally, we detail a baseline process to employ our framework in practice, leveraging large language models to expedite the generation of tailored concrete syntax. We illustrate the potential of our vision with two concrete examples and discuss the shortcomings and research challenges of current intelligent generation techniques.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".