Classical data flow analysis determines whether a data flow fact may hold or does not hold at some program point. Probabilistic data flow systems compute a range, i.e. a probability, with which a data flow fact will hold at some program point. In this paper we develop a novel, practicable framework for probabilistic data flow problems. In contrast to other approaches, we utilize execution history for calculating the probabilities of data flow facts. In this way we achieve significantly better results. Effectiveness and efficiency of our approach are shown by compiling and running the SPECint95 benchmark suite.

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