Extending concern-oriented reuse to existing 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
Modern software systems constitute remarkably large and complex entities made up of an intricate web of components and libraries that render their development exclusively with source code ill-advised.Model-driven engineering promises to alleviate such issues by making models fundamental to all development phases.These models are to be specified according to appropriate and relevant modelling languages with the right level of abstraction while emphasizing a strict separation of concerns.MDE also emphasizes reusing existing standardized models and modelled design patterns to simplify the design process and increase productivity.Unfortunately, reuse is far from common in actual modelling practice, too often development teams will prefer using completely new models, at the cost of both time and effort, either because they have specific notation needs or are unaware of the potential benefits of reuse.Existing modelling frameworks and tools do little to facilitate this task, lacking intuitive and efficient reuse mechanisms and providing arcane interfaces to reuse and tune languages from the modelling community.Hence we propose, in the following thesis, our contribution to improve support for modelling reuse and language tailoring by extending the Concern Oriented Reuse (CORE) modelling framework to support multiple external languages and augmenting them with language independent reuse capabilities.Furthermore, with our proposed concept of perspectives, we would allow a language designer to tailor languages for a specific purpose.These perspectives also pave the road for concern-oriented multi-view modelling, as they can be designed to orchestrate the combined use of multiple languages to frame a design process.By redesigning and improving the existing reuse oriented CORE modelling framework through the addition of the language concept, we allow it to support any external abstract syntax, i.e. language, defined with a metamodel.Furthermore, CORE languages have a novel manner of describing their semantics through language actions, enabling our proposed concept of perspectives to i To Jrg, whose near-infinite wisdom, knowledge, wholesomeness and enduring patience form the main reason I was able to carry this project to fruition.To my dear parents, sister and brother, who stood by my, supported and helped me throughout all of my studies and projects.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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