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The ability to assess the reliability of safety-critical systems is one of the most crucial requirements in the design of modern safety-critical systems where even a minor failure can result in loss of life or irreparable damage to the environment. Model checking is an automatic technique that verifies or refutes system properties by exploring all reachable states (state space) of a model. In large and complex systems, it is probable that the state space explosion problem occurs. In exploring the state space of systems modeled by graph transformations, the rule applied on the current state specifies the rule that can perform on the next state. In other words, the allowed rule on the current state depends only on the applied rule on the previous state, not the ones on earlier states. This fact motivates us to use a Markov chain (MC) to capture this type of dependencies and applies the Estimation of Distribution Algorithm (EDA) to improve the quality of the MC. EDA is an evolutionary algorithm directing the search for the optimal solution by learning and sampling probabilistic models through the best individuals of a population at each generation. To show the effectiveness of the proposed approach, we implement it in GROOVE, an open source toolset for designing and model checking graph transformation systems. Experimental results confirm that the proposed approach has a high speed and accuracy in comparison with the existing meta-heuristic and evolutionary techniques in safety analysis of systems specified formally through graph transformations.

Safety is one of the most important features of modern software systems, especially safety-critical systems such as nuclear power plants, which can be checked exactly by model checking. Model checking is a formal verification technique that analyzes system properties through exploring all reachable states (state space) of a model of a system. The problem of the technique is that it confronts the state space explosion in large and complex systems due to exponential memory usage. Recent researches show that a partial and intelligent exploration of the state space can be a suitable solution to overcome this problem. In this paper, we propose a two-phase model checking for safety analysis of systems specified formally through graph transformations. In the first phase, the beam-search algorithm explores the state space to a specific number of states. In case of failure of the phase, the second phase starts: in systems specified through graph transformations, the rule applied on the previous state can determine the rule that can perform on the next state. In other words, the rule on current state depends on only the rule applied to previous state, not the one on earlier states. Hence, a Markov chain (MC) is estimated to capture dependencies between the sequence of applied rules in the state space explored by the beam-search algorithm. The MC is then employed to explore the remainder of the state space intelligently. To evaluate the effectiveness of the two-phase model checking, we implement it in GROOVE, an open source toolset for designing and model checking graph transformation systems. Experimental results show that the two-phase model checking has the high speed and accuracy in comparison with the existing meta-heuristic and evolutionary techniques.

Model checking is one of the successful techniques in automated verification of software and hardware systems. However, employing this technique for verification of properties such as the safety and liveness requires that all possible states of a system are generated and then the given property is checked. In large and complex systems, generating all states causes the state space explosion problem. Hence, it is possible to refute such properties by searching the state space to find a state in which the given property is violated. Of course, it is possible that detecting such states in complex systems leads to the exhaustive exploration of the state space. Recent researches confirm that using meta-heuristic and evolutionary approaches to intelligently explore a portion of the state space can be a promising idea. Hence, in this paper, we propose an approach based on ant colony optimization algorithm for refuting the safety and liveness properties and also analyzing reachability ones in systems specified formally through graph transformation system. Refutation of liveness and analyzing reachability properties in systems modeled by graph transformations are the prominent advantages of this approach in comparison with the previous approaches. The proposed approach is implemented in GROOVE which is an open source toolset for designing and model checking graph transformation systems. Experimental results show that our proposed approach is faster and it generates shorter counterexamples in comparison with others.

While developing concurrent systems, one of the important properties to be checked is deadlock freedom. Model checking is an accurate technique to detect errors, such as deadlocks. However, the problem of model checking in complex software systems is state space explosion in which all reachable states cannot be generated due to exponential memory usage. When a state space is too large to be explored exhaustively, using meta-heuristic and evolutionary approaches seems a proper solution to address this problem. Recently, a few methods using genetic algorithm, particle swarm optimization and similar approaches have been proposed to handle this problem. Even though the results of recent approaches are promising, the accuracy and convergence speed may still be a problem. In this paper, a novel method is proposed using Bayesian Optimization Algorithm (BOA) to detect deadlocks in systems specified formally through graph transformations. BOA is an Estimation of Distribution Algorithm in which a Bayesian network (as a probabilistic model) is learned from the population and then sampled to generate new solutions. Three different structures are considered for the Bayesian network to investigate deadlocks in the benchmark problems. To evaluate the efficiency of the proposed approach, it is implemented in GROOVE, an open source toolset for designing and model checking graph transformation systems. Experimental results show that the proposed approach is faster and more accurate than existing algorithms in discovering deadlock states in the most of case studies with large state spaces.

It is important to find optimal solutions for structural errors in rule-based expert systems .Solutions to discovering such errors by using model checking techniques have already been proposed, but these solutions have problems such as state space explosion. In this paper, to overcome these problems, we model the rule-based systems as finite state transition systems and express confliction and unreachability as Computation Tree Logic (CTL) logic formula and then use the technique of model checking to detect confliction and unreachability in rule-based systems with the model checker UPPAAL., 7 pages