Conformance Testing And Inference Of Embedded Components

The problems of active inference (learning) and conformance testing of a system modelled by an automaton have actively been studied for decades, however, much less attention has been paid to modular systems, modelled by communicating automata. In this paper, we consider a system of two communicating FSMs, one machine represents an embedded component and another the remaining part of the system, the context. Assuming that the context FSM is known, we want to learn the embedded FSM without directly interacting with it. This problem can be viewed as a generalization of the classical automata inference in isolation, i.e., it is the grey box learning problem. The proposed approach to solve this problem relies on a SAT-solving method for FSM inference from traces. It does not depend on the composition topology and allows at the same time to solve a related problem of conformance testing in context. The latter is to test whether an embedded implementation FSM composed with the given context is equivalent to the embedded specification FSM also composed with the context. The novelty of the conformance testing method is that it directly generates a complete test suite for the embedded machine and avoids using nondeterministic approximations with their tests, eliminating thus several sources of test redundancy inherent in the existing methods.