Communications service synthesis from informal specifications and sequence diagrams.
Bibliographic record
Abstract
Communications Networks consist of layers where each layer provides a service to the layer above. A service consists of phases each of which is a sequence of message interactions intended to achieve a specific user goal [Boyc 90]. For example, Transport Service has three phases: connection establishment, data transfer and connection release. Recent research [Dsou 95] and [Sale 96] has emphasized the need for systematic methods to build services. We propose such a method to build a Global Service FSM (Finite State Machine) from a set of Sequence Diagrams. We use these diagrams to capture a set of constraints on the service: local, end-to-end and concurrency constraints. The service synthesis method is composed of a series of algorithmic steps. The two major steps are: (i) construction of Phase Scenario Machines (PSM), (ii) coupling of PSMs at the phase boundaries. The resulting FSM, called a Global Scenario Machine (GSM), is a more precise and complete model of the global service. This thesis shows that a global service specification, the Global Scenario Machine can be semiautomatically and systematically built from a set of sequence diagrams. More work is needed on tools and verification methods to insure the completeness of this approach and its ease of use, but a realistic case study is used illustrate the feasibility of the approach.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".