Inferring FSM models of systems without reset

Abstract

Active inference algorithms that are used to extract behavioural models of software systems usually assume that the System Under Inference (SUI) can be reset. Two approaches have been proposed to infer systems that cannot be reset. Rivest and Schapire proposed an adaptation of the L* algorithm that relies on having a homing sequence for the SUI. We detail here another approach that is based on characterization sequences. More precisely, we assume classical testing hypotheses, namely that we are given a bound n on the number of states and a set W of characterizing sequences to distinguish states. Contrary to L*, it does not require an external oracle to decide on equivalence. The length of the test sequence is polynomial in n and the exponent depends on the cardinality |W| of the characterization set. For systems where resetting is impossible or expensive, this approach can be a viable alternative to classical learning methods.