Model-Based Development with Distributed Cognition
Bibliographic record
Abstract
Model Driven Engineering (MDE) allows a system to be defined using a series of models. It can refine higher level models into lower level models using model transformations, thereby automating the building of a concrete model and the software development process. This is particularly useful for Requirements Engineering since MDE can bridge the gap between early requirements models, late requirements models, and architectural models. However, requirements elicitation techniques have received little attention in terms of MDE. A major reason is the lack of a formal modelling language for some of these techniques. The definition of a metamodel is an essential step for the specification of a formal modeling language, which is a key prerequisite for Model Driven Engineering (MDE). We introduce a metamodel for Distributed Cognition, a well-known requirements elicitation technique, using the key concepts present in the framework's literature with the aim to integrate Distributed Cognition into MDE. Furthermore, we report on a preliminary case study on a software XP team to validate our metamodel.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".