Efficient Path-Sensitive Data-Dependence Analysis

TL;DR

This paper introduces a scalable, path- and context-sensitive data-dependence analysis that efficiently handles aliasing and disjunctive reasoning, improving precision and scalability in large codebases.

Contribution

It proposes a novel sparse, demand-driven approach that combines symbolic value-flow graphs with transitive dependence resolution, addressing aliasing-path explosion effectively.

Findings

Significant improvements in analysis precision.

Enhanced scalability demonstrated on large codebases.

Outperforms state-of-the-art techniques in experiments.

Abstract

This paper presents a scalable path- and context-sensitive data-dependence analysis. The key is to address the aliasing-path-explosion problem via a sparse, demand-driven, and fused approach that piggybacks the computation of pointer information with the resolution of data dependence. Specifically, our approach decomposes the computational efforts of disjunctive reasoning into 1) a context- and semi-path-sensitive analysis that concisely summarizes data dependence as the symbolic and storeless value-flow graphs, and 2) a demand-driven phase that resolves transitive data dependence over the graphs. We have applied the approach to two clients, namely thin slicing and value flow analysis. Using a suite of 16 programs ranging from 13 KLoC to 8 MLoC, we compare our techniques against a diverse group of state-of-the-art analyses, illustrating significant precision and scalability advantages of our approach.