We describe action machines, a framework for encoding and composing partial behavioral descriptions. Action machines encode behavior as a variation of labeled transition systems where the labels are observable activities of the described artifact and the states capture full data models. Labels may also have structure, and both labels and states may be partial with a symbolic representation of the unknown parts. Action machines may stem from software models or programs, and can be composed in a variety of ways to synthesize new behaviors. The composition operators described here include synchronized and interleaving parallel composition, sequential composition, and alternating simulation. We use action machines in analysis processes such as model checking and model-based testing. The current main application is in the area of model-based conformance testing, where our approach addresses practical problems users at Microsoft have in applying model-based testing technology.
Abstract. We present a novel approach for generating interaction combinations based on SMT constraint resolution. Our approach can generate maximal interaction coverage in the presence of general constraints as supported by the underlying solver. It supports seeding with general predicates, which allows us to combine it with path exploration such that both interaction and path coverage goals can be met. Our approach is motivated by the application to behavioral model-based testing in the Spec Explorer tool, where parameter combinations must be generated such that all path conditions of a model action have at least one combination which enables the path. It is applied in a large-scale project for model-based quality assurance of interoperability documentation at Microsoft.
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