Modeling Traceability for Heterogeneous Systems
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
In System Engineering, many systems encompass widely different domains of expertise; there are several challenges in relating these domains due to their heterogeneity and complexity. Although, literature provides many techniques to model traceability among heterogeneous domains, existing solutions are either tailored to specific domains (e.g., Ecore modeling languages), or not complete enough (e.g., lack support to specify traceability link semantics). This paper proposes a generic traceability model that is not domain specific; it provides a solution for modeling traceability links among heterogeneous models, that is, systems for which traceability links need to be established between artifacts in widely different modeling languages (e.g., UML, block diagrams, informal documents). Our solution tackles the drawbacks of existing solutions, and incorporates some of their ideas in an attempt to be as complete as possible. We argue that our solution is extensible in the sense that it can adapt to new modeling languages, new ways of characterizing traceability information for instance, without the need to change the model itself.
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 | 0.001 | 0.001 |
| 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