Path-Sensitive Data Flow Analysis Simplified

نویسندگان

  • Kirsten Winter
  • Chenyi Zhang
  • Ian J. Hayes
  • Nathan Keynes
  • Cristina Cifuentes
  • Lian Li
چکیده

Path-sensitive data flow analysis pairs classical data flow analysis with an analysis of feasibility of paths to improve precision. In this paper we propose a framework for path-sensitive backward data flow analysis that is enhanced with an abstraction of the predicate domain. The abstraction is based on a three-valued logic. It follows the strategy that path predicates are simplified if possible (without calling an external predicate solver) and every predicate that could not be reduced to a simple predicate is abstracted to the unknown value, for which the feasibility is undecided. The implementation of the framework scales well and delivers promising results.

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تاریخ انتشار 2013