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.

Authors: Yue Li, Tian Tan, Anders Moller, Yannis Smaragdakis. 2020.

In ACM Transactions on Programming Languages and Systems (TOPLAS ‘20). Vol. 42,
No. 2, Article 10.

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.

Read the PDF:
A Principled Approach to Selective Context Sensitivity for Pointer Analysis - TOPLAS