Methods for Designing SIP Features in SDL with Fewer Feature Interactions.
Bibliographic record
Abstract
This paper describes methods for implementing telephony services in SIP with fewer traditional feature interactions. A formal SDL model of SIP and its services has been derived from published SIP specifications for verification and validation. It is known that the SIP RFC describes only the protocol specification. The specifications of SIP services and additional service features are informal and can only be found in various IETF drafts. Nevertheless, the service designers are still faced with new feature interaction problems. These new feature interactions are unique to SIP because SIP has flexible signaling features, such as request forking and dynamic assignment of contact addresses, which have both cooperative and adversarial side effects on each other. This paper also describes an extension to the classical feature interaction taxonomy, which is used to associate the causes, effects/symptoms with the preventive measures of the new and traditional feature interactions. Finally, SIP services can be designed and implemented without certain feature interactions by following certain design rules which are based on the knowledge deduced from the verification.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".