Runtime Enforcement of Web Service Message Contracts with Data
Bibliographic record
Abstract
An increasing number of popular SOAP web services exhibit a stateful behavior, where a successful interaction is determined as much by the correct format of messages as by the sequence in which they are exchanged with a client. The set of such constraints forms a "message contract” that needs to be enforced on both sides of the transaction; it often includes constraints referring to actual data elements inside messages. We present an algorithm for the runtime monitoring of such message contracts with data parameterization. Their properties are expressed in {\rm LTL}\hbox{-}{\rm FO}^+, an extension of Linear Temporal Logic that allows first-order quantification over the data inside a trace of XML messages. An implementation of this algorithm can transparently enforce an {\rm LTL}\hbox{-}{\rm FO}^+ specification using a small and invisible Java applet. Violations of the specification are reported on-the-fly and prevent erroneous or out-of-sequence XML messages from being exchanged. Experiments on commercial web services from Amazon.com and Google indicate that {\rm LTL}\hbox{-}{\rm FO}^+ is an appropriate language for expressing their message contracts, and that its processing overhead on sample traces is acceptable both for client-side and server-side enforcement architectures.
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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 | 0.011 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".