Specialized 3-Valued Logic Shape Analysis Using Structure-Based Refinement and Loose Embedding

  • Gilad Arnold
Conference paper

DOI: 10.1007/11823230_14

Part of the Lecture Notes in Computer Science book series (LNCS, volume 4134)
Cite this paper as:
Arnold G. (2006) Specialized 3-Valued Logic Shape Analysis Using Structure-Based Refinement and Loose Embedding. In: Yi K. (eds) Static Analysis. SAS 2006. Lecture Notes in Computer Science, vol 4134. Springer, Berlin, Heidelberg


We consider a shape analysis framework based on 3-valued logic, and explore ways for improving its performance and scalability by means of reducing algorithmic overhead and restraining abstract state set inflation. First we propose a new approach to implementing a fast 3-valued logic analyzer, which replaces a general-purpose abstract heap refinement mechanism—accounting for most of the time spent by the reference implementation—with tailored structure-based refinement. We apply our framework to analyze a set of small Java programs manipulating singly- and doubly-linked lists, obtaining results that are comparable to those of the reference implementation, with a process 40-85 times faster and 2-11 times less memory consuming. We then propose a new definition for partial ordering of abstract heap descriptors (embedding), that trims abstract states representing “special cases” in the presence of a state representing a “general case”. This extension deflates sets of abstract states by a combinatorial factor, resulting in 45-55% less structures for the same set of benchmarks. Despite its induced algorithmic overhead per operation, this modification further cuts the analysis time by 17-50%. We argue that improving on these two axes together yields a promise for greater applicability of specialized shape analysis to real-life programs.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Gilad Arnold
    • 1
  1. 1.University of CaliforniaBerkeley

Personalised recommendations