Model Checking In Order Research Articles

A Layered and Parallelized Method of Eventual Model Checking

Termination or halting is an important system requirement that many systems should satisfy and can be expressed in linear temporal logic as eventual properties. We devised a divide-and-conquer approach to eventual model checking in order to reduce the state space explosion in model checking. The idea of the technique is to split an original model checking problem for eventual properties into multiple smaller model checking problems and handle each smaller one. Due to the nature of the divide-and-conquer approach, each smaller model checking problem can essentially be tackled independently. Hence, this paper proposes a parallel technique/tool based on a master–worker pattern for the divide-and-conquer approach to model checking eventual properties. We carry out some experiments to show the effectiveness of our parallel technique/tool, which can somewhat enhance the running performance to a certain extent when conducting model checking for eventual properties.

An Innovative Approach to e-public Tendering Based on Model Checking

Abstract The public construction sector has recently started to put more effort into cost savings and improvement of efficiency. Therefore, public clients have started to promote new strategies, embracing innovative approaches such as Building Information Modelling (BIM) and e-Procurement. Tendering plays a key role for the success of the overall process; however, the selection of the best contractor is still a difficult task. The paper investigates how tendering can be integrated with Model Checking in order to control the compliance between the client's requirements and bidders’ offers within a digital environment. The research shows that, although the BIM is still not widely adopted in tendering procedures, Model Checking tools are already available to support the jury, as well as bidders. Moreover, even though BIM can be implemented in several procurement procedures, it is more effective if a collaborative and integrated behaviour is promoted. Results can help increase public clients’ awareness of the limits and potentials of an innovative approach and to set BIM requirements and guidelines.

A Systematic Approach to Model Checking Human–Automation Interaction Using Task Analytic Models

Formal methods are typically used in the analysis of complex system components that can be described as “automated” (digital circuits, devices, protocols, and software). Human-automation interaction has been linked to system failure, where problems stem from human operators interacting with an automated system via its controls and information displays. As part of the process of designing and analyzing human-automation interaction, human factors engineers use task analytic models to capture the descriptive and normative human operator behavior. In order to support the integration of task analyses into the formal verification of larger system models, we have developed the enhanced operator function model (EOFM) as an Extensible Markup Language-based, platform- and analysis-independent language for describing task analytic models. We present the formal syntax and semantics of the EOFM and an automated process for translating an instantiated EOFM into the model checking language Symbolic Analysis Laboratory. We present an evaluation of the scalability of the translation algorithm. We then present an automobile cruise control example to illustrate how an instantiated EOFM can be integrated into a larger system model that includes environmental features and the human operator's mission. The system model is verified using model checking in order to analyze a potentially hazardous situation related to the human-automation interaction.

A Method for the Formal Verification of Human-interactive Systems

Predicting failures in complex, human-interactive systems is difficult as they may occur under rare operational conditions and may be influenced by many factors including the system mission, the human operator's behavior, device automation, human-device interfaces, and the operational environment. This paper presents a method that integrates task analytic models of human behavior with formal models and model checking in order to formally verify properties of human-interactive systems. This method is illustrated with a case study: the programming of a patient controlled analgesia pump. Two specifications, one of which produces a counterexample, illustrate the analysis and visualization capabilities of the method.

A Method for the Formal Verification of Human-interactive Systems

Predicting failures in complex, human-interactive systems is difficult as they may occur under rare operational conditions and may be influenced by many factors including the system mission, the human operator's behavior, device automation, human-device interfaces, and the operational environment. This paper presents a method that integrates task analytic models of human behavior with formal models and model checking in order to formally verify properties of human-interactive systems. This method is illustrated with a case study: the programming of a patient controlled analgesia pump. Two specifications, one of which produces a counterexample, illustrate the analysis and visualization capabilities of the method.

TIMED ANALYSIS OF EMBEDDED NETWORKS USING TIMED AUTOMATA

Increasing communication needs of embedded systems lead to the integration of switched Ethernet, in order to bypass the bandwidth limitation of classical fieldbuses. This paper propose the use of timed automata and model checking in order to analyze the timing behavior of a network architecture including specific fieldbuses (CAN, FIP, …) interconnected by switched Ethernet. The architecture is decomposed into basic components modeled by timed automata. The modeling of the overall architecture is obtained by assembling basic components. Model checking then exhibits for instance worst-case delays scenarios.

Combining Symmetry Reduction and Under-Approximation for Symbolic Model Checking

This work presents a collection of methods that integrate symmetry reduction and under-approximation with symbolic model checking in order to reduce space and time. The main objective of these methods is falsification. However, under certain conditions, they can provide verification as well.We first present algorithms that use symmetry reduction to perform on-the-fly model checking for temporal safety properties. These algorithms avoid building the orbit relation and choose representatives on-the-fly while computing the reachable states. We then extend these algorithms to check liveness properties as well. In addition, we introduce an iterative on-the-fly algorithm that builds subsets of the orbit relation rather than the full relation.Our methods are fully automatic once the user supplies some basic information about the symmetry in the verified system. Moreover, the methods are robust and work correctly even if the information supplied by the user is incorrect. Furthermore, the methods return correct results even when the computation of the symmetry reduction has not been completed due to memory or time explosion.We implemented our methods within the IBM model checker Rule-Base and compared their performance to that of RuleBase. In most cases, our algorithms outperformed RuleBase in both time and space.

Combining partial order reductions with on-the-fly model-checking

Partial order model-checking is an approach to reduce time and memory in model-checking concurrent programs. On-the-fly model-checking is a technique to eliminate part of the search by intersecting an automaton representing the (negation of the) checked property with the state during its generation. We prove conditions under which these two methods can be combined in order to gain reduction from both. An extension of the model-checker SPIN, which implements this combination, is studied, showing substantial reduction over traditional search, not only in the number of reachable states, but directly in the amount of memory and time used. We also describe how to apply partial-order model-checking under given fairness assumptions.