Bridging concrete and abstract syntaxes in model‐driven engineering: a case of rule languages
Bibliographic record
Abstract
Abstract The paper covers the problem of bridging the gap between abstract and textual concrete syntaxes of software languages in the model‐driven engineering (MDE) context. This problem has been well studied in the context of programming languages, but due to the obvious difference in the definitions of abstract syntax, MDE requires a new set of engineering principles. We first explore different approaches to defining abstract and concrete syntaxes in the MDE context. Next, we investigate the current state of languages and techniques used for bridging between textual concrete and abstract syntaxes in the context of MDE. Finally, we report on lessons learned in experimenting with the current technologies. In order to provide a comprehensive coverage of the problem under study, we have selected a case of Web rule languages. Web rule languages leverage various types of syntax specification languages; and they are complex in nature and large in terms of the language elements. Thus, they provide us with a realistic analysis framework based on which we can draw general conclusions. Based on the series of experiments that we conducted with the analyzed languages, we propose a method for approaching such problems and report on the empirical results obtained from the data collected during our experiments. Copyright © 2009 John Wiley & Sons, Ltd.
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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 | 0.033 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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".