A schema for interprocedural modification side-effect analysis with pointer aliasing

  • Authors:

  • Barbara G. Ryder;William A. Landi;Philip A. Stocks;Sean Zhang;Rita Altucher

  • Affiliations:

  • Rutgers University;Siemens Corporate Research, Inc.;Rutgers University;Rutgers University;Rutgers University

  • Venue:
  • ACM Transactions on Programming Languages and Systems (TOPLAS)
  • Year:
  • 2001

Quantified Score

Hi-index 0.00

Visualization

Abstract

The first interprocedural modification side-effects analysis for C (MODC) that obtains better than worst-case precision on programs with general-purpose pointer usage is presented with empirical results. The analysis consists of an algorithm schema corresponding to a family of MODC algorithms with two independent phases: one for determining pointer-induced aliases and a subsequent one for propagating interprocedural side effects. These MODC algorithms are parameterized by the aliasing method used. The empirical results compare the performance of two dissimilar MODC algorithms: MODC(FSAlias) uses a flow-sensitive, calling-context-sensitive interprocedural alias analysis; MODC(FIAlias uses a flow-insensitive, calling-context-insensitive alias analysis which is much faster, but less accurate. These two algorithms were profiled on 45 programs ranging in size from 250 to 30,000 lines of C code, and the results demonstrate dramatically the possible cost-precision trade-offs. This first comparative implementation of MODC analyses offers insight into the differences between flow-/context-sensitive and flow-/context-insensitive analyses. The analysis cost versus precision trade-offs in side-effect information obtained are reported. The results show surprisingly that the precision of flow-sensitive side-effect analysis is not always prohibitive in cost, and that the precision of flow-insensitive analysis is substantially better than worst-case estimates and seems sufficient for certain applications. On average MODC(FSAlias) for procedures and calls is in the range of 20% more precise than MODC(FIAlias); however, the performance was found to be at least an order of magnitude slower than MODC(FIAlias).