RelationalAI is named a Cool Vendor in the May 2022 Gartner Cool Vendors in Augmented Data Management report.

A Principled Approach to Selective Context Sensitivity for Pointer Analysis - TOPLAS

RelationalAI

01 January 2020

less than a minute read

A Principled Approach to Selective Context Sensitivity for Pointer Analysis - TOPLAS

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.

Related Posts

Get Early Access

Join our community, keep up to date with the latest developments in our monthly newsletter, and get early access to RelationalAI.