Narrowing the gaps in Concern-Driven Development
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
Concern-Driven Development (CDD) promises improved productivity, reusability, and maintainability because high-level concerns that are important to stakeholders are encapsulated regardless of how these concerns are distributed over the system structure. However, to truly capitalize on the benefits promised by CDD, concerns need to be encapsulated across software development phases, i.e., across different types of models at different levels of abstraction. Model-Driven Engineering plays an important role in this context as the automated transformation of concern-oriented models (a) allows a software engineer to use the most appropriate modeling notation for a particular task, (b) automates error-prone tasks, and (c) avoids duplication of modeling effort. The earlier transformations can be applied in a CDD process, the greater the potential cost savings. Hence, we report on our experiences in applying tool supported transformations from scenario-based requirements models to structural and behavioral design models during CDD. While automated model transformations certainly contribute to the three benefits mentioned above, they can also lead to more clearly and succinctly defined modeling activities at each modeling level and aid in the precise definition of the semantics of the used modeling notations.
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