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In this paper, we show how to automate the generation of an\ninput grammar suitable for input fuzzing using sample inputs and\nneural-network-based statistical machine-learning techniques. We present a\ndetailed case study with a complex input format, namely PDF, and a large\ncomplex security-critical parser for this format, namely, the PDF parser\nembedded in Microsoft's new Edge browser. We discuss (and measure) the tension\nbetween conflicting learning and fuzzing goals: learning wants to capture the\nstructure of well-formed inputs, while fuzzing wants to break that structure in\norder to cover unexpected code paths and find bugs. 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