Derivation of Stochastic Reward Net for Compatibility and Conformance Verification of Component Erroneous Behavior Model
Bibliographic record
Abstract
The compatibility verification between interacting components and the conformance verification of their internal behavior with the corresponding ports protocol behavior are important steps for the early identification of unexpected messages between components. The behavior models used for verification include erroneous behavior along with normal behavior, in order to ensure greater accuracy in reliability and availability analysis. We use our Component Erroneous Behavior Aspect Modeling (CeBAM) approach introduced in previous work, which applies aspect-oriented modeling for adding erroneous behavior to UML state machines representing normal behavior. In this paper we present transformation rules for deriving Stochastic Reward Net (SRN) from CeBAM representations. The first step is to generate SRN for individual component behavior in order to check the conformance between component internal behavior and their ports protocol behavior. Subsequently, we compose the generated SRNs models of the connected components to verify their compatibility. We show how to identify conformance and compatibility issues during the construction and composition of components SRN model by analyzing SRN properties (e.g., deadlocks). We illustrate the proposed verification approach through a case study modeled according to CeBAM.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".