Context sensitivity is an essential technique for ensuring high precision in static analyses. It has been observed that applying context sensitivity partially, only on a select subset of the methods, can improve the balance between analysis precision and speed. However, existing techniques are based on heuristics that do not provide much insight into what characterizes this method subset. In this work, we present a more principled approach for identifying precision-critical methods, based on general patterns of value flows that explain where most of the imprecision arises in context-insensitive pointer analysis. Using this theoretical foundation, we present an efficient algorithm, Z IPPER , to recognize these flow patterns in a given program and employ context sensitivity accordingly. We also present a variant, Z IPPER e , that additionally takes into account which methods are disproportionally costly to analyze with context sensitivity. Our experimental results on standard benchmark and real-world Java programs show that Z IPPER preserves effectively all of the precision (98.8%) of a highly precise conventional context-sensitive pointer analysis (2-object-sensitive with a context-sensitive heap, 2obj for short), with a substantial speedup (on average, 3.4× and up to 9.4×), and that Z IPPER e preserves 94.7% of the precision of 2obj, with an order-of-magnitude speedup (on average, 25.5× and up to 88×). In addition, for 10 programs that cannot be analyzed by 2obj within a three-hour time limit, on average Z IPPER e can guide 2obj to finish analyzing them in less than 11 minutes with high precision compared to context-insensitive and introspective context-sensitive analyses.

@article{LiTMS20,
  title = {A Principled Approach to Selective Context Sensitivity for Pointer Analysis},
  author = {Yue Li and Tian Tan and Anders Møller and Yannis Smaragdakis},
  journal = {ACM Transactions on Programming Languages and Systems},
  volume = {42},
  number = {2},
  year = {2020},
  doi = {10.1145/3381915}
}