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Automata-based modeling languages, like Component Interaction Automata, offeran attractive means to capture and analyze the behavioral aspects ofinteracting components. At the center of these modeling languages we findfinite state machines that allow for a fine-grained description how and whenspecific service requests may interact with other components or theenvironment. Unfortunately, automata-based approaches suffer from exponentialstate explosion, a major obstacle to the successful application of theseformalisms in modeling real-world scenarios. In order to cope with thecomplexity of individual specifications we can apply partition refinement, anabstraction technique to alleviate the state explosion problem. But thistechnique too exhibits exponential time and space complexity and, worse, doesnot offer any guarantees for success. To better understand as to why partitionrefinement succeeds in some cases while it fails in others, we conducted anempirical study on the performance of a partition refinement algorithm forComponent Interaction Automata specifications. As a result we have identifiedsuitable predictors for the expected effectiveness of partition refinement. Itis the structure, not the size, of a specification that weighs heavier on theoutcome of partition refinement. In particular, Component Interaction Automataspecifications for real-world systems are capable of producing scale-freenetworks containing structural artifacts that can assist the partitionrefinement algorithm not only converge earlier, but also yield a significantstate space reduction on occasion.

Partition Refinement of Component Interaction Automata: Why Structure Matters More Than Size