Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

11-2022

Abstract

Symbolic execution (SE) has been widely adopted for automatic program analysis and software testing. Many SE engines (e.g., KLEE or Angr) need to interpret certain Intermediate Representations (IR) of code during execution, which may be slow and costly. Although a plurality of studies proposed to accelerate SE, few of them consider optimizing the internal interpretation operations. In this paper, we propose FastKLEE, a faster SE engine that aims to speed up execution via reducing redundant bound checking of type-safe pointers during IR code interpretation. Specifically, in FastKLEE, a type inference system is first leveraged to classify pointer types (i.e., safe or unsafe) for the most frequently interpreted read/write instructions. Then, a customized memory operation is designed to perform bound checking for only the unsafe pointers and omit redundant checking on safe pointers. We implement FastKLEE on top of the well-known SE engine KLEE and combined it with the notable type inference system CCured. Evaluation results demonstrate that FastKLEE is able to reduce by up to 9.1% (5.6% on average) as the state-of-the-art approach KLEE in terms of the time to explore the same number (i.e., 10k) of execution paths. FastKLEE is opensourced at https://github.com/haoxintu/FastKLEE. A video demo of FastKLEE is available at https://youtu.be/fjV_a3kt-mo.

Keywords

Performance, Software testing, Symbolic execution, Type inference

Discipline

Computer Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, Singapore, 2022 November 14 - 18

ISBN

9781450394130

Identifier

10.1145/3540250.3558919

Publisher

Association for Computing Machinery

City or Country

Singapore

Additional URL

https://doi.org/10.1145/3540250.3558919

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