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Cost effective dynamic program slicing

Although dynamic program slicing was first introduced to aid in user level debugging, applications aimed at improving software quality, reliability, security, and performance have since been identified as candidates for using dynamic slicing. However, the dynamic dependence graph constructed to compute dynamic slices can easily cause slicing algorithms to run out of memory for realistic program runs. In this paper we present the design and evaluation of a cost effective dynamic program slicing algorithm. This algorithm is based upon a dynamic dependence graph representation that is highly compact and rapidly traversable. Thus, the graph can be held in memory and dynamic slices can be quickly computed. A compact representation is derived by recognizing that all dynamic dependences (data and control) need not be individually represented. We identify sets of dynamic dependence edges between a pair of statements that can share a single representative edge. We further show that the dependence graph can be transformed in a manner that increases sharing and sharing can be performed even in the presence of aliasing. Experiments show that transformed dynamic dependence graphs explicitly represent only 6% of the dependence edges present in the full dynamic dependence graph. When the full graph sizes range from 0.84 to 1.95 Gigabytes in size, our compacted graphs range from 20 to 210 Megabytes in size. Average slicing times for our algorithm range from 1.74 to 36.25 seconds across several benchmarks from SPECInt2000/95.