Detecting Dependability Failures in Healthcare Scenarios via Digital Shadows

In healthcare systems, practitioners are responsible for making decisions when a patient's health, or even life, are at stake. Real-time data-driven modeling, analysis, and prediction approaches, such as the Digital Shadow (DS) paradigm, inform and support decision-makers in such critical situa...

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Veröffentlicht in:Proceedings - International Symposium on Software Reliability Engineering S. 502 - 513
Hauptverfasser: Guindani, Bruno, Camilli, Matteo, Lestingi, Livia, Bersani, Marcello Maria
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 21.10.2025
Schlagworte:
Automata Learning
Cyber-Physical Systems
Fuzzing
Human Digital Shadow
Learning automata
Load modeling
Medical services
Predictive models
Safety
Software reliability
Space exploration
Statistical Model Checking
Systematics
Uncertainty
ISSN:2332-6549
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Zusammenfassung:In healthcare systems, practitioners are responsible for making decisions when a patient's health, or even life, are at stake. Real-time data-driven modeling, analysis, and prediction approaches, such as the Digital Shadow (DS) paradigm, inform and support decision-makers in such critical situations. We introduce GENGAR, a DS-based methodology to identify critical scenarios in a patient-device-physician (PDP) triad with human agents and cyber-physical devices interacting under uncertainty. The proposed solution relies on automata-based modeling and formal analysis techniques to predict and inform the practitioner of critical contingencies that may compromise patient safety, enhancing the system's dependability. In particular, it leverages automata learning to infer and evolve a realistic patient model from clinical logs. GENGAR then exploits mutational and searchbased fuzzing to generate scenarios and detect failure cases, i.e., those violating predefined dependability requirements. Failure scenarios are then filtered using qualitative criteria on clinical plausibility, yielding up to 60 \% realistic cases.
ISSN:2332-6549
DOI:10.1109/ISSRE66568.2025.00056