Improving System Reliability via Model Checking: The FSAP/NuSMV-SA Safety Analysis Platform

Bozzano, Marco; Villafiorita, Adolfo

doi:10.1007/978-3-540-39878-3_5

Cited by 74 publications

(54 citation statements)

References 22 publications

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“…Also, our approach is the first and, as far as we know, currently only one that relates the Halpern and Pearl model of causation to the model of transition system and which considers the ordering of events to be potentially causal. In [7,8], a symbolic approach to generate Fault Trees [23] is presented. In this approach all single component failures have to be known in advance while in our approach these failures are computed as a result of the algorithm.…”

Section: Related Workmentioning

confidence: 99%

Symbolic Causality Checking Using Bounded Model Checking

Beer

Heidinger

Kühne

et al. 2015

Model Checking Software

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Abstract. In precursory work we have developed causality checking, a fault localization method for concurrent system models relying on the Halpern and Pearl counterfactual model of causation that identifies ordered occurrences of system events as being causal for the violation of non-reachability properties. Our first implementation of causality checking relies on explicit-state model checking. In this paper we propose a symbolic implementation of causality checking based on bounded model checking (BMC) and SAT solving. We show that this BMC-based implementation is efficient for large and complex system models. The technique is evaluated on industrial size models and experimentally compared to the existing explicit state causality checking implementation. BMC-based causality checking turns out to be superior to the explicit state variant in terms of runtime and memory consumption for very large system models.

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Section: Related Workmentioning

confidence: 99%

Symbolic Causality Checking Using Bounded Model Checking

Beer

Heidinger

Kühne

et al. 2015

Model Checking Software

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“…This becomes a severe limitation when the system is complex and analysts may overlook some possible hazards. Safety-critical systems are getting more and more complex and, thus, there is a trend to use methods [4], [14] that are more automatic and exhaustive than hazard analysis, for example, model checking.…”

Section: Related Workmentioning

confidence: 99%

“…The verification of safety-critical systems using formal techniques is not something new [19], as can be seen from methods such as state machine hazard analysis, which was based on Petri nets [20], and the application of model checking to safety-critical system verification based on various formal models such as finite state machines [4], Statecharts [3], Process Control Event Diagrams [28], Scade [8], and Altarica [3]. A common method for the application of model checking to safety-critical system verification is through the specification of safety-related properties using some temporal logic such as Computation Tree Logic (CTL) or Linear Temporal Logic (LTL) and then checking for the satisfaction of the safety specification [15].…”

Section: Related Workmentioning

confidence: 99%

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Model Checking Safety-Critical Systems Using Safecharts

Hsiung

Chen

Lin

2007

IEEE Trans. Comput.

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Abstract-With rapid developments in science and technology, we now see the ubiquitous use of different types of safety-critical systems in our daily lives such as in avionics, consumer electronics, and medical systems. In such systems, unintentional design faults might result in injury or even death to human beings. To make sure that safety-critical systems are really safe, there is a need to verify them formally. However, the verification of such systems is getting more and more difficult because designs are becoming very complex. To cope with high design complexity, currently, model-driven architecture design is becoming a well-accepted trend. However, existing methods of testing and standards conformance are restricted to implementation code, so they do not fit very well with model-based approaches. To bridge this gap, we propose a model-based formal verification technique for safety-critical systems. In this work, the model-checking paradigm is applied to the Safecharts model, which was used for modeling but not yet used for verification. Our contributions listed are as follows: First, the safety constraints in Safecharts are mapped to semantic equivalents in timed automata for verification. Second, the theory for safety constraint verification is proven and implemented in a compositional model checker (that is, the State-Graph Manipulator (SGM)). Third, prioritized and urgent transitions are implemented in SGM to model the risk semantics in Safecharts. Finally, it is shown that the priority-based approach to mutual exclusion of resource usage in the original Safecharts is unsafe and corresponding solutions are proposed here. Application examples show the feasibility and benefits of the proposed model-driven verification of safety-critical systems.

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“…These other automated approaches seem to us to suffer from their dependence upon modeling formalisms that lack semantics that are sufficient to represent complex processes clearly, completely, and precisely. Different from these approaches, some approaches, such as [10] and [11], use model checking to generate fault trees. They require explicit state machine models to represent the faults that can occur within components.…”

Section: Limitationsmentioning

confidence: 99%

Automatic Fault Tree Derivation from Little-JIL Process Definitions

Chen

Avrunin

Clarke

et al. 2006

Software Process Change

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Abstract. Defects in safety critical processes can lead to accidents that result in harm to people or damage to property. Therefore, it is important to find ways to detect and remove defects from such processes. Earlier work has shown that Fault Tree Analysis (FTA) [3] can be effective in detecting safety critical process defects. Unfortunately, it is difficult to build a comprehensive set of Fault Trees for a complex process, especially if this process is not completely welldefined. The Little-JIL process definition language has been shown to be effective for defining complex processes clearly and precisely at whatever level of granularity is desired [1]. In this work, we present an algorithm for generating Fault Trees from Little-JIL process definitions. We demonstrate the value of this work by showing how FTA can identify safety defects in the process from which the Fault Trees were automatically derived.

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Improving System Reliability via Model Checking: The FSAP/NuSMV-SA Safety Analysis Platform

Cited by 74 publications

References 22 publications

Symbolic Causality Checking Using Bounded Model Checking

Symbolic Causality Checking Using Bounded Model Checking

Model Checking Safety-Critical Systems Using Safecharts

Automatic Fault Tree Derivation from Little-JIL Process Definitions

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