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Obtaining accurate system models for verification is a hard and time consuming process, which is seen by industry as a hindrance to adopt otherwise powerful modeldriven development techniques and tools. In this paper we pursue an alternative approach where an accurate high-level model can be automatically constructed from observations of a given black-box embedded system. We adapt algorithms for learning finite probabilistic automata from observed system behaviors. We prove that in the limit ofdoi:10.1109/qest.2011.21 dblp:conf/qest/MaoCJNLN11 fatcat:nakxdm27xzbvjmb25mdogt2yjy