Demand-driven memory leak detection based on flow- and context-sensitive pointer analysis

  • Authors:

  • Ji Wang;Xiao-Dong Ma;Wei Dong;Hou-Feng Xu;Wan-Wei Liu

  • Affiliations:

  • National Laboratory for Parallel and Distributed Processing, National University of Defense Technology, Changsha, China;National Laboratory for Parallel and Distributed Processing, National University of Defense Technology, Changsha, China;National Laboratory for Parallel and Distributed Processing, National University of Defense Technology, Changsha, China;National Laboratory for Parallel and Distributed Processing, National University of Defense Technology, Changsha, China;National Laboratory for Parallel and Distributed Processing, National University of Defense Technology, Changsha, China

  • Venue:
  • Journal of Computer Science and Technology
  • Year:
  • 2009

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Abstract

We present a demand-driven approach to memory leak detection algorithm based on flow- and context-sensitive pointer analysis. The detection algorithm firstly assumes the presence of a memory leak at some program point and then runs a backward analysis to see if this assumption can be disproved. Our algorithm computes the memory abstraction of programs based on points-to graph resulting from flow- and context-sensitive pointer analysis. We have implemented the algorithm in the SUIF2 compiler infrastructure and used the implementation to analyze a set of C benchmark programs. The experimental results show that the approach has better precision with satisfied scalability as expected.