Incomplete Adaptive Distinguishing Sequences for Non-Deterministic FSMs

The increasing complexity and criticality of software systems have led to growing interest in automated test generation. One of the most promising approaches is to use model-based testing (MBT), in which test automation is based on a model of the
implementation under test (IUT)
, with much of the work concerning finite state machine (FSM) models. Many FSM-based test generation techniques use, possibly adaptive, sequences to check the state of the IUT. Of particular interest are adaptive distinguishing sequences (ADSs) because their use can lead to relatively small tests. However, not all systems possess an ADS. In this work, we generalise the notion of incomplete ADSs to non-deterministic partial and observable FSMs. We show that the problem of checking the existence of a set of
$k$
incomplete ADSs that separates every pair of states is PSPACE-hard. Further, we generalise the notion of invertible sequences to non-deterministic partial and observable FSMs and show how invertible sequences can be used to derive additional incomplete ADSs. We propose a novel algorithm to generate incomplete ADSs and describe the results of experiments that evaluated its performance. The results indicate that the proposed method can generate sequences to identify states of the IUT and is faster and can process larger FSMs than other existing methods.