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.
Bag Query Containment and Information Theory
The query containment problem is a fundamental algorithmic problem in data management. While this problem is well understood under set semantics, it is by far less understood under bag semantics. In this paper we unveil tight connections between information theory and the conjunctive query containment under bag semantics.
Computer Vision: Deep Dive into Object Segmentation Approaches
Join optimization has been dominated by Selinger-style, pairwise optimizers for decades. But, Selinger-style algorithms are asymptotically suboptimal for applications in graphic analytics. This suboptimality is one of the reasons that many have advocated supplementing relational engines with specialized graph processing engines.