Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

8-2021

Abstract

Array constraints are prevalent in analyzing a program with symbolic execution. Solving array constraints is challenging due to the complexity of the precise encoding for arrays. In this work, we propose to synergize symbolic execution and array constraint solving. Our method addresses the difficulties in solving array constraints with novel ideas. First, we propose a lightweight method for pre-checking the unsatisfiability of array constraints based on integer linear programming. Second, observing that encoding arrays at the byte-level introduces many redundant axioms that reduce the effectiveness of constraint solving, we propose type and interval aware axiom generation. Note that the type information of array variables is inferred by symbolic execution, whereas interval information is calculated through the above pre-checking step. We have implemented our methods based on KLEE and its underlying constraint solver STP and conducted large-scale experiments on 75 real-world programs. The experimental results show that our method effectively improves the efficiency of symbolic execution. Our method solves 182.56% more constraints and explores 277.56% more paths on average under the same time threshold.

Keywords

Software and its engineering, Software testing and debugging

Discipline

Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

Proceedings of the 30th ACM SIGSOFT International Symposium on Software Testing and Analysis, Virtual Conference, 2021 July 11-17

First Page

361

Last Page

373

ISBN

9781450384599

Identifier

10.1145/3460319.3464826

Publisher

ACM

City or Country

New York

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