Empirical study of optimization techniques for massive slicing

  • Authors:

  • David Binkley;Mark Harman;Jens Krinke

  • Affiliations:

  • Loyola College in Maryland, Baltimore, MD;King's College London, London, UK;FernUniversität in Hagen, Hagen, Germany

  • Venue:
  • ACM Transactions on Programming Languages and Systems (TOPLAS)
  • Year:
  • 2007

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Abstract

This article presents results from a study of techniques that improve the performance of graph-based interprocedural slicing of the System Dependence Graph (SDG). This is useful in “massive slicing” where slices are required for many or all of the possible set of slicing criteria. Several different techniques are considered, including forming strongly connected components, topological sorting, and removing transitive edges. Data collected from a test bed of just over 1,000,000 lines of code are presented. This data illustrates the impact on computation time of the techniques. Together, the best combination produces a 71% reduction in run-time (and a 64% reduction in memory usage). The complete set of techniques also illustrates the point at which faster computation is not viable due to prohibitive preprocessing costs.