Geometric encoding
Xiao Xiao, Charles Zhang · 2011
Context sensitive points-to analysis suffers from the scalability problem. We present the geometric encoding to capture the redundancy in the points-to analysis. Compared to BDD and EPA, the state of the art, the geometric encoding is much more efficient in processing the encoded facts, especially for the high-order context sensitivity with the heap cloning. We also developed two precision preserving techniques, constraints distillation and 1-CFA SCC modeling, to further improve the efficiency, in addition to the precision performance trade-off scheme. We evaluate our points-to algorithm with two variants of the geometric encoding, Geom and HeapIns, on 15 widely cited Java benchmarks. The evaluation shows that the Geom based algorithm is 11x and 68x faster than the worklist/BDD based 1-object-sensitive analysis in Paddle, and the speedup steeply goes up to 24x and 111x, if the HeapIns algorithm is used. Meanwhile, being very efficient in time, the precision is still equal to and sometime better than the 1-object-sensitive analysis.