Cost and precision tradeoffs of dynamic data slicing algorithms

  • Authors:

  • Xiangyu Zhang;Rajiv Gupta;Youtao Zhang

  • Affiliations:

  • The University of Arizona, Tucson, AZ;The University of Arizona, Tucson, AZ;University of Texas at Dallas, Richardson, TX

  • Venue:
  • ACM Transactions on Programming Languages and Systems (TOPLAS)
  • Year:
  • 2005

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Abstract

Dynamic slicing algorithms are used to narrow the attention of the user or an algorithm to a relevant subset of executed program statements. Although dynamic slicing was first introduced to aid in user level debugging, increasingly applications aimed at improving software quality, reliability, security, and performance are finding opportunities to make automated use of dynamic slicing. In this paper we present the design and evaluation of three precise dynamic data slicing algorithms called the full preprocessing (FP), no preprocessing (NP) and limited preprocessing (LP) algorithms. The algorithms differ in the relative timing of constructing the dynamic data dependence graph and its traversal for computing requested dynamic data slices. Our experiments show that the LP algorithm is a fast and practical precise data slicing algorithm. In fact we show that while precise data slices can be orders of magnitude smaller than imprecise dynamic data slices, for small number of data slicing requests, the LP algorithm is faster than an imprecise dynamic data slicing algorithm proposed by Agrawal and Horgan.