Approximate Active Learning of Nondeterministic Input Output Transition Systems

Constructing a model of a system for model-based testing, simulation, or model checking can be cumbersome for existing, third party, or legacy components. Active automata learning, a form of black-box reverse engineering, and in particular Angluin’s L algorithm, support the automatic inference of a model from a System Under Learning (SUL), through observations and tests. Most of the algorithms based on L, however, deal with complete learning of deterministic models, thus being unable to cope with nondeterministic SULs, and always learning a complete and correct model as they are based on equivalence between the SUL and the model. We present an adaptation of Angluin’s algorithm for active learning of nondeterministic, input-enabled, input-output transition systems. It enables dealing with nondeterministic SULs, and it allows to construct partial, or approximate models, by expressing the relation between the SUL and the learned model as a refinement relation, not necessarily an equivalence. Thus, we can reason about learned models being more, or less precise than others. Approximate learning has benefits in model-based regression testing: we need not to wait until a complete model has been learned; with an approximate model ioco-based regression testing can start.