Model Checking Single Web Services using Markov Chains and MDPs
Bibliographic record
Abstract
This paper proposes a new verification method for individual web service specified using OWL-S, the Ontology Web Language for semantic web Services, where probabilities are introduced to model uncertain choices. The approach makes use of a probabilistic model checker named PRISM. The web service description is received as an input in form of OWL-S, which is analyzed and automatically transformed into a Discrete Time Markov Chain (DTMC) or a Markov Decision Process (MDP). The obtained DTMC/MDP is then processed and coded into the PRISM language. This code is parsed and verified by the PRISM model checker to determine which required properties are satisfied by the web service. In addition to the atomic processes, different composite processes are considered including sequence, choice, if-then-else, repeat-while, repeat-until, split, and split join. We also prove that the transformation algorithm is sound and complete and introduce a software tool implementing the approach.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".