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The Complexity of Andersen’s Analysis in Practice

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Static Analysis (SAS 2009)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 5673))

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Abstract

While the tightest proven worst-case complexity for Andersen’s points-to analysis is nearly cubic, the analysis seems to scale better on real-world codes. We examine algorithmic factors that help account for this gap. In particular, we show that a simple algorithm can compute Andersen’s analysis in worst-case quadratic time as long as the input program is k-sparse, i.e. it has at most k statements dereferencing each variable and a sparse flow graph. We then argue that for strongly-typed languages like Java, typical structure makes programs likely to be k-sparse, and we give empirical measurements across a suite of Java programs that confirm this hypothesis. We also discuss how various standard implementation techniques yield further constant-factor speedups.

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Author information

Authors and Affiliations

  1. IBM T.J. Watson Research Center, USA

    Manu Sridharan & Stephen J. Fink

Authors
  1. Manu Sridharan
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  2. Stephen J. Fink
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Editor information

Editors and Affiliations

  1. Department of Computer Science, University of California, Los Angeles, 4531K Boelter Hall, 90095-1596, Los Angeles, CA, USA

    Jens Palsberg

  2. Department of Computer Science, University of California, 1 Shields Avenue, 95616, Davis, CA, USA

    Zhendong Su

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© 2009 Springer-Verlag Berlin Heidelberg

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Sridharan, M., Fink, S.J. (2009). The Complexity of Andersen’s Analysis in Practice. In: Palsberg, J., Su, Z. (eds) Static Analysis. SAS 2009. Lecture Notes in Computer Science, vol 5673. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03237-0_15

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  • DOI: https://doi.org/10.1007/978-3-642-03237-0_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-03236-3

  • Online ISBN: 978-3-642-03237-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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