Automatic Generation of Test Cases of Multi-Agent Systems Based on Model Checking
Weijian Tang, Keming Wang · 2021 16th International Conference on Intelligent Systems and Knowledge Engineering (ISKE) · 2021
The reliability of a train control system depends on the consistency between the system under the test cases and the design specification. It is vital to ensure consistency by verifying that the functions of the train control system meet the design specifications. This paper introduces a novel and formal approach of test cases generation that guarantees the reliability of a train control system. The proposed methodology is effectively applied to a reactive, concurrent and complex case study of the train control system, namely the level transition, and uses multi-agent systems to describe each system component as an intelligent agent. We use the model check tool, NuSMV, that supports formal specifications, modelling the communication among agents. Furthermore, this paper combines computation tree logic with modified condition/decision coverage to design the trap properties. Based on the model checking technology, the test sequence is automatically generated by verifying the trap properties. Lastly, a set of functional test cases that satisfy the transition coverage are obtained by revising the test sequence. The research results show that the method in this paper can be used to generate test cases that are executed to verify the consistency between the system under testing and the design specification.