Multi-Layer Consistency Validation of IoT Systems with UML Inheritance Dynamic Diagrams via SPIN Model Checking
Bibliographic record
Abstract
The integration of the Unified Modeling Language (UML) with the Internet of Things (IoT) facilitates the multi-faceted modeling of complex IoT systems.Despite existing methodologies addressing UML coherence, the literature reveals a paucity of strategies for ensuring consistency between use cases and their manifestations in activity and sequence diagrams, particularly when inheritance is employed.This study delves into the validation of UML behavioral views, focusing on the coherence of use cases, activity diagrams, and sequence diagrams within IoT specifications through a multi-layered consistency approach.A methodology is presented for transforming IoT system specifications into Bchi automata, enabling consistency verification through the SPIN Model Checker.The robustness of this method is demonstrated through a case study involving a Healthcare IoT system, highlighting the utility of the proposed validation technique.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 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".