Visual Analysis of Hyperproperties for Understanding Model Checking Results

Horak, Tom; Coenen, Norine; Niklas, Metzger,; Hahn, Christopher; Flemisch, Tamara; Méndez, Julián; Dimov, Dennis; Finkbeiner, Bernd; Dachselt, Raimund

doi:10.1109/tvcg.2021.3114866

Cited by 7 publications

(8 citation statements)

References 61 publications

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“…The Model Checker checks if a property is verified on each patient model [ 38 , 39 ]. This logic rule includes a logical-temporal reasoning [ 37 ] that is needed to link different CT slices, so it guarantees a multi-slice approach.…”

Section: Methodsmentioning

confidence: 99%

Early Diagnosis of Liver Metastases from Colorectal Cancer through CT Radiomics and Formal Methods: A Pilot Study

Rocca

Brunese

Santone

et al. 2021

JCM

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Background: Liver metastases are a leading cause of cancer-associated deaths in patients affected by colorectal cancer (CRC). The multidisciplinary strategy to treat CRC is more effective when the radiological diagnosis is accurate and early. Despite the evolving technologies in radiological accuracy, the radiological diagnosis of Colorectal Cancer Liver Metastases (CRCLM) is still a key point. The aim of our study was to define a new patient representation different by Artificial Intelligence models, using Formal Methods (FMs), to help clinicians to predict the presence of liver metastasis when still undetectable using the standard protocols. Methods: We retrospectively reviewed from 2013 to 2020 the CT scan of nine patients affected by CRC who would develop liver lesions within 4 months and 8 years. Seven patients developed liver metastases after primary staging before any liver surgery, and two patients were enrolled after R0 liver resection. Twenty-one patients were enrolled as the case control group (CCG). Regions of Interest (ROIs) were identified through manual segmentation on the medical images including only liver parenchyma and eventual benign lesions, avoiding major vessels and biliary ducts. Our predictive model was built based on formally verified radiomic features. Results: The precision of our methods is 100%, scheduling patients as positive only if they will be affected by CRCLM, showing a 93.3% overall accuracy. Recall was 77.8%. Conclusion: FMs can provide an effective early detection of CRCLM before clinical diagnosis only through non-invasive radiomic features even in very heterogeneous and small clinical samples.

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Section: Methodsmentioning

confidence: 99%

Early Diagnosis of Liver Metastases from Colorectal Cancer through CT Radiomics and Formal Methods: A Pilot Study

Rocca

Brunese

Santone

et al. 2021

JCM

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“…We implemented Alg. 1 and evaluated it on publicly available example instances of HyperVis [47], for which their state graphs were available. In the following, we provide implementation details, report on the running time and show the usefulness of the implementation by comparing to the highlighting output of HyperVis.…”

Section: Implementation and Experimentsmentioning

confidence: 99%

“…Comparison to HyperVis. HyperVis [47] is a tool for visualizing counterexamples returned from the HyperLTL model checker MCHyper [34]. It highlights the events in the trace that it considers responsible for the violation based on the formula and the set of traces, without considering the system model.…”

Section: Implementation and Experimentsmentioning

confidence: 99%

“…A first attempt in explaining model checking results of HyperLTL specifications has been made with HyperVis [47], which visualizes a counterexample returned by the model checker MCHyper [34] in a browser application. While the visualizations are already useful to analyze the counterexample at hand, it fails to identify causes for the violation in several security-critical scenarios.…”

Section: Introductionmentioning

confidence: 99%

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Explaining Hyperproperty Violations

Coenen¹,

Dachselt²,

Finkbeiner³

et al. 2022

Preprint

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Hyperproperties relate multiple computation traces to each other. Model checkers for hyperproperties thus return, in case a system model violates the specification, a set of traces as a counterexample. Fixing the erroneous relations between traces in the system that led to the counterexample is a difficult manual effort that highly benefits from additional explanations. In this paper, we present an explanation method for counterexamples to hyperproperties described in the specification logic HyperLTL. We extend Halpern and Pearl's definition of actual causality to sets of traces witnessing the violation of a HyperLTL formula, which allows us to identify the events that caused the violation. We report on the implementation of our method and show that it significantly improves on previous approaches for analyzing counterexamples returned by HyperLTL model checkers.

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“…A first attempt in explaining model checking results of HyperLTL specifications has been made with HyperVis [48], which visualizes a counterexample returned by the model checker MCHyper [35] in a browser application. While the visualizations are already useful to analyze the counterexample at hand, it fails to identify causes for the violation in several security-critical scenarios.…”

Section: Introductionmentioning

confidence: 99%

Explaining Hyperproperty Violations

Coenen

Dachselt

Finkbeiner

et al. 2022

Computer Aided Verification

Self Cite

View full text Add to dashboard Cite

Hyperproperties relate multiple computation traces to each other. Model checkers for hyperproperties thus return, in case a system model violates the specification, a set of traces as a counterexample. Fixing the erroneous relations between traces in the system that led to the counterexample is a difficult manual effort that highly benefits from additional explanations. In this paper, we present an explanation method for counterexamples to hyperproperties described in the specification logic HyperLTL. We extend Halpern and Pearl’s definition of actual causality to sets of traces witnessing the violation of a HyperLTL formula, which allows us to identify the events that caused the violation. We report on the implementation of our method and show that it significantly improves on previous approaches for analyzing counterexamples returned by HyperLTL model checkers.

show abstract

Visual Analysis of Hyperproperties for Understanding Model Checking Results

Cited by 7 publications

References 61 publications

Early Diagnosis of Liver Metastases from Colorectal Cancer through CT Radiomics and Formal Methods: A Pilot Study

Early Diagnosis of Liver Metastases from Colorectal Cancer through CT Radiomics and Formal Methods: A Pilot Study

Explaining Hyperproperty Violations

Explaining Hyperproperty Violations

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