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.

We show how the paradigm of learning-based testing (LBT)can be applied to automate specification-based black-box testing of reactivesystems. Since reactive systems can be modeled as Kripke structures,we introduce an efficient incremental learning algorithm IKL forsuch structures. We show how an implementation of this algorithm combinedwith an efficient model checker such as NuSMV yields an effectivelearning-based testing architecture for automated test case generation(ATCG), execution and evaluation, starting from temporal logic requirements.