A Domain Specific Language for the Models and Data (MODA) Framework in Model-Driven Engineering
Bibliographic record
Abstract
Over the years, software engineering technology has improved and advanced massively, and as such, it has greatly impacted the development cycle of systems.Some systems can now even be developed entirely with little human involvement with the help of model-driven engineering techniques.Recently, systems have become very data-centric; hence there is the need to understand if these systems can be built in a model-driven way and how data-centric approaches fit into this picture.The Models and Data (MODA) framework is a conceptual reference framework that clarifies the various roles that models and data play in software development and operation of sociotechnical systems.Using this framework, we are able to view the various parts of the system that serve as models and different kinds of data, analyze the role they each play, and finally understand how they work together to make the system function.The authors of the MODA framework outline the architecture and vast applicability of the framework but there is currently no tool support to help practitioners build MODA models.Also, the broad applicability of the framework is claimed but only preliminary evidence is given.This thesis introduces a domain-specific language and tool support for the MODA framework.As there is currently no existing metamodel for the framework, this thesis contributes to the existing framework with a well-defined metamodel that accurately depicts the framework's elements.To further validate this metamodel, a tool was built using the Xtext and Sirius language engineering environments.This tool enables modelers to create their own MODA models to represent the socio-technical system they would want to analyze.Furthermore, an evaluation of the proof-of-concept tool and the MODA framework with the help of an educationbased exploratory study gives further evidence of the broad applicability of the MODA framework by representing key courses in the Applied Artificial Intelligence minor and Software Engineering 1.3.Thesis Overview Observations and analysis are carried out to try and identify areas that the MODA framework does not capture and how effective the framework is in modeling many situations.While the MODA framework generally allows the courses to be captured well, the analysis reveals aspects of the framework that may need additional investigation or expansion beyond the existing definitions provided by the original MODA framework [31].1.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".