Model-Based Development with Distributed Cognition
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) 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.
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 | 0.000 | 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