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This paper presents a scalable path- and context-sensitive data dependence analysis. The key is to address the aliasing-path-explosion problem when enforcing a path-sensitive memory model. 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, piggybacking the computation of fully path-sensitive pointer information with the resolution of data dependence of interest. We have applied the approach to two clients, namely thin slicing and value-flow bug finding. Using a suite of 16 C/C++ programs ranging from 13 KLoC to 8 MLoC, we compare our techniques against a diverse group of state-of-the-art analyses, illustrating the significant precision and scalability advantages of our approach.
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Peisen Yao
Jinguo Zhou
Xiao Xiao
Proceedings of the ACM on Programming Languages
University of Hong Kong
Zhejiang University
Hong Kong University of Science and Technology
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Yao et al. (Thu,) studied this question.
www.synapsesocial.com/papers/68e63f5cb6db6435875d0d86 — DOI: https://doi.org/10.1145/3656400
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