YANG-APR: Towards Supporting Evolution in Model-driven Network Management Systems
Bibliographic record
Abstract
In model-driven network ecosystems, high-level specifications such as YANG data models define APIs that engineers extend with custom code and handlers. When the model evolves, it can become misaligned with its API implementation, which typically requires manual, costly, and error-prone realignment, highlighting the need for automated repair. This paper presents our ongoing work on an automated repair approach to resynchronize evolving YANG models with their API implementations, using a four-stage pipeline: (i) localize compatible-vs-breaking diffs between the current and updated models, (ii) enrich each diff record with contextual information, (iii) instantiate precise transformation rules for every change, and (iv) apply those rules to generate a repaired version of the code, complete with a reviewable change log for engineer validation. To explore the feasibility of this approach, we have developed an initial prototype-YANG-APR-that targets five common model evolution scenarios: datatype change, node rename, endpoint URL rename, endpoint removal, and endpoint addition. We illustrate the approach through two representative cases: a breaking datatype modification and a non-breaking endpoint addition. These early results show promising potential to facilitate model-implementation coevolution, providing a foundation for continued development and broader evaluation.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".