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We present a new active model-learning approach to generating abstractions ofa system implementation, as finite state automata (FSAs), from executiontraces. Given an implementation and a set of observable system variables, thegenerated automata admit all system behaviours over the given variables andprovide useful insight in the form of invariants that hold on theimplementation. To achieve this, the proposed approach uses a pluggable modellearning component that can generate an FSA from a given set of traces.Conditions that encode a completeness hypothesis are then extracted from theFSA under construction and used to evaluate its degree of completeness bychecking their truth value against the system using software model checking.This generates new traces that express any missing behaviours. The new tracedata is used to iteratively refine the abstraction, until all system behavioursare admitted by the learned abstraction. To evaluate the approach, wereverse-engineer a set of publicly available Simulink Stateflow models fromtheir C implementations.

Active Learning of Abstract System Models from Traces using Model Checking [Extended]