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A Novel Probabilistic Data Flow Framework

  • Eduard Mehofer
  • Bernhard Scholz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2027)

Abstract

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.

Keywords

Equation System Probabilistic Data Data Fact Outgoing Edge Incoming Edge 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Eduard Mehofer
    • 1
  • Bernhard Scholz
    • 2
  1. 1.Institute for Software ScienceUniversity of ViennaAustria
  2. 2.Institute of Computer LanguagesVienna University of TechnologyAustria

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