Reference
Abstract
When constructing complex program analyses, it is often useful to reason about not just individual values, but collections of values. Symbolic set abstractions provide building blocks that can be used to partition elements, relate partitions to other partitions, and determine the provenance of multiple values, all without knowing any concrete values. To address the simultaneous challenges of scalability and precision, we formalize and implement an interface for symbolic set abstractions and construct multiple abstract domains relying on both specialized data structures and off-the-shelf theorem provers. We develop techniques for lifting existing domains to improve performance and precision. We evaluate these domains on real-world data structure analysis problems.
BibTeX
@string{LPAR = "International Conference on Logic for Programming, Artificial Intelligence and Reasoning (LPAR)"}
@inproceedings{setr-lpar15,
author = {Arlen Cox and Bor-Yuh Evan Chang and Huisong Li and Xavier Rival},
title = {Abstract Domains and Solvers for Sets Reasoning},
booktitle = LPAR,
year = {2015},
pages = {356-371},
}