Symbolic Discord Computation for Efficient Analysis of Message Sequence Charts

Message sequence charts (MSCs) and high-level MSCs (HMSCs) have been standardized to model interactions of parallel processes as message exchanges. We can flexibly express parallel behaviors with MSCs, but alternatively, it is possible to put unintended orders of messages into the MSCs. This paper focuses on detection of such unintended orders as discord. We propose an encoding scheme in which the analysis of an HMSC is converted into a boolean SAT problem. Experimental results show that our approach achieves efficient analysis of HMSCs which have a large number of processes or a large size of graphs. This contributes efficient analysis of specification on complex interactions.