Model Learning and Test Generation Using Cover Automata

We propose an approach which, given a state-transition model of a system, constructs, in parallel, an approximate automaton model and a test suite for the system. The approximate model construction relies on a variant of Angluin's automata learning algorithm, adapted to finite cover automata. A finite cover automaton represents an approximation of the system that only considers sequences of length up to an established upper bound l. Crucially, the size of the cover automaton, which normally depends on l, can be significantly lower than the size of the exact automaton model. Thus, controlling l, the state explosion problem normally associated with constructing and checking state-based models can be mitigated. The proposed approach also allows for a gradual construction of the model and of the associated test suite, with complexity and time savings. Moreover, we provide automation of counterexample search, by a combination of black-box and random testing, and metrics to evaluate the quality of the produced results. The approach is presented and implemented in the context of the Event-B modeling language, but its underlying ideas and principles are much more general and can be applied to any system whose behavior can be suitably described by a state-transition model.