On-demand dynamic summary-based points-to analysis

  • Authors:

  • Lei Shang;Xinwei Xie;Jingling Xue

  • Affiliations:

  • The University of New South Wales, NSW, Australia;The University of New South Wales, NSW, Australia;The University of New South Wales, NSW, Australia

  • Venue:
  • Proceedings of the Tenth International Symposium on Code Generation and Optimization
  • Year:
  • 2012

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Abstract

Static analyses can be typically accelerated by reducing redundancies. Modern demand-driven points-to or alias analysis techniques rest on the foundation of Context-Free Language (CFL) reachability. These techniques achieve high precision efficiently for a small number of queries raised in small programs but may still be too slow in answering many queries for large programs in a context-sensitive manner. We present an approach, called DynSum, to perform context-sensitive demand-driven points-to analysis fully on-demand by means of computing CFL-reachability summaries without any precision loss. The novelty lies in initially performing a Partial Points-To Analysis (PPTA) within a method, which is field-sensitive but context-independent, to summarize its local points-to relations encountered during a query and reusing this information later in the same or different calling contexts. We have compared DynSum with RefinePTS, a refinement-based analysis, using three clients (safe casting, null dereferencing and factory methods) for a suite of nine Java programs. DynSum's average speedups are 1.95x, 2.28x and 1.37x, respectively. We have also compared DynSum with a static approach, which is referred to StaSum here, to show its improved scalability for the same three clients.