  We present a new algorithm IDS for incremental learning of deterministic
finite automata (DFA). This algorithm is based on the concept of distinguishing
sequences introduced in (Angluin81). 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 IDS is
an efficient algorithm for software engineering applications of automata
learning, such as testing and model inference.
