FIDDLR: streamlining reuse with concern-specific modelling languages
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Model-Driven Engineering (MDE) reduces complexity, improves Separation of Concerns and promotes reuse by structuring software development as a process of model production and refinement. Domain-Specific Modelling Languages and Aspect-Oriented Modelling techniques can reduce complexity and improve modularization of crosscutting concerns in situations where the features of general purpose modelling languages are not well aligned with the subject of study. In this article we present FIDDLR, a novel framework that integrates the ideas of Domain-Specific Modelling Languages, Concern-Oriented Reuse and MDE to modularize concerns that cross-cut multiple levels of abstraction of the software development process and streamline the reuse process. It also prescribes the integration of the different tooling along this process. We demonstrate the effectiveness of our framework and the potential for reduced complexity and leveraged reuse by building a reusable concern that exposes the services a system offers through a REST interface.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it