Context-Sensitive Relevancy Analysis for Efficient Symbolic Execution

  • Authors:

  • Xin Li;Daryl Shannon;Indradeep Ghosh;Mizuhito Ogawa;Sreeranga P. Rajan;Sarfraz Khurshid

  • Affiliations:

  • School of Information Science, Japan Advanced Institute of Science and Technology, Nomi, Japan;Department of Electrical and Computer Engineering, University of Texas at Austin, Austin, USA;Trusted Systems Innovation Group, Fujitsu laboratory of America, Sunnyvale, USA;School of Information Science, Japan Advanced Institute of Science and Technology, Nomi, Japan;Trusted Systems Innovation Group, Fujitsu laboratory of America, Sunnyvale, USA;Department of Electrical and Computer Engineering, University of Texas at Austin, Austin, USA

  • Venue:
  • APLAS '08 Proceedings of the 6th Asian Symposium on Programming Languages and Systems
  • Year:
  • 2008

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Abstract

Symbolic execution is a flexible and powerful, but computationally expensive technique to detect dynamic behaviors of a program. In this paper, we present a context-sensitive relevancy analysis algorithm based on weighted pushdown model checking, which pinpoints memory locations in the program where symbolic values can flow into. This information is then utilized by a code instrumenter to transform only relevant parts of the program with symbolic constructs, to help improve the efficiency of symbolic execution of Java programs. Our technique is evaluated on a generalized symbolic execution engine that is developed upon Java Path Finder with checking safety properties of Java applications. Our experiments indicate that this technique can effectively improve the performance of the symbolic execution engine with respect to the approach that blindly instruments the whole program.