A Model Partitioning Method in Model Checking for Information and Control System

Information and control systems operated in the field of social infrastructure are required to enhance their quality in terms of safety and reliability, and model checking is an effective technique to validate their behavior in the design phase. Model checking generates a state transition diagram from a model of system behavior and verifies that the model satisfies a system requirement by exploring the state space. However, as the number of model attributes and attribute value combinations increases, the state space expands, leading to a state explosion that makes completing the search within a realistic time impossible. To solve this problem, methods to reduce the state space by dividing the model are commonly applied, although these methods require human judgment based on knowledge of the system and the designer’s experience. The purpose of this paper is to propose a method for partitioning behavioral models of information and control system (ICS) without relying on such judgment. The structures of ICS are represented by attributes, and the behaviors are described by rules using these attributes. The description includes attributes that are characteristics of an ICS. This method extracts dependency relationships between rules from the reference to the attribute and generates the dependency graph. The graph is partitioned by clustering into clusters corresponding to the rules, thus reducing the state space. Clustering partitions the model at points where relationships between clusters, such as rule dependencies, are sufficiently low. Modularity is used as a measure to ensure that the total number of states after partitioning is less than before. The authors will confirm the effectiveness of this method by using the ICS example to show the partitioning of the system using this method, compare the number of states in the behavior models generated from the partitioned system, and show the results of model checking using these behavior models.