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arXiv · 2109.07923

Efficient Path-Sensitive Data-Dependence Analysis

Peisen Yao·Jinguo Zhou·Xiao Xiao·Qingkai Shi·Rongxin Wu·Charles Zhang
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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.

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Peisen Yao, Jinguo Zhou, Xiao Xiao, Qingkai Shi, Rongxin Wu, Charles Zhang. 2021-09-16. Efficient Path-Sensitive Data-Dependence Analysis. https://arxiv.org/abs/2109.07923

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