A Model Traceability Framework for Network Service Management
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
Automating enactment along with traceability management of processes using model-driven engineering methods could be of significant benefit to the Network Functions Virtualization (NFV) paradigm in view of its move towards zero-touch automation of the orchestration and management of network services (NS). Earlier, we proposed an integrated process modelling and enactment environment with traceability support, MAPLE-T, for NS management. In this paper, we extend MAPLE-T with the notion of intents. We propose the usage of intents at both the process model (PM) and model-transformation levels as part of our traceability information. We define intents as information representing the objective of the PM actions/activities and their implementations. We extend MAPLE-T with traceability visualization support to visualize trace links relating models at different levels through the captured intents. The intent-enriched traceability information and the enhanced visualization enable semantically richer traceability analysis. We apply our traceability generation and analysis approach to the NS design process in order to show the benefits of intents not only for the process, but also for the whole NS lifecycle management operations.
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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