Engineering of Framework-Specific Modeling Languages
Bibliographic record
Abstract
Framework-specific modeling languages (FSMLs) help developers build applications based on object-oriented frameworks. FSMLs model abstractions and rules of application programming interfaces (APIs) exposed by frameworks and can express models of how applications use APIs. Such models aid developers in understanding, creating, and evolving application code. We present four exemplar FSMLs and a method for engineering new FSMLs. The method was created postmortem by generalizing the experience of building the exemplars and by specializing existing approaches to domain analysis, software development, and quality evaluation of models and languages. The method is driven by the use cases that the FSML under development should support and the evaluation of the constructed FSML is guided by two existing quality frameworks. The method description provides concrete examples for the engineering steps, outcomes, and challenges. It also provides strategies for making engineering decisions. Our work offers a concrete example of software language engineering and its benefits. FSMLs capture existing domain knowledge in language form and support application code understanding through reverse engineering, application code creation through forward engineering, and application code evolution through round-trip engineering.
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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 | 0.017 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".