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We present a new algorithm IDS for incremental learning of deterministicfinite automata (DFA). This algorithm is based on the concept of distinguishingsequences introduced in (Angluin81). We give a rigorous proof that two versionsof this learning algorithm correctly learn in the limit. Finally we present anempirical performance analysis that compares these two algorithms, focussing onlearning times and different types of learning queries. We conclude that IDS isan efficient algorithm for software engineering applications of automatalearning, such as testing and model inference.

IDS: An Incremental Learning Algorithm for Finite Automata