We present a new algorithm IDSfor incremental learning of deterministic finite automata (DFA). This algorithm is based on the concept of distinguishing sequences introduced in [Angluin 1981]. We give a rigorous proof that two versions of this learning algorithm correctly learn in the limit. Finally we present an empirical performance analysis that compares these two algorithms, focussing on learning times and different types of learning queries. We conclude that IDSis an efficient algorithm for software engineering applications of automata learning, such as testing and model inference.
This thesis concerns the design, implementation and evaluation of a specification based testing architecture for reactive systems using the paradigm of learning-based testing. As part of this work we have designed, verified and implemented new incremental learning algorithms for DFA and Kripke structures.These have been integrated with the NuSMV model checker to give a new learning-based testing architecture. We have evaluated our architecture on case studies and shown that the method is effective.