SYMTUNER: Maximizing the Power of Symbolic Execution by Adaptively Tuning External Parameters

Sooyoung Cha, Myungho Lee, Seokhyun Lee, Hakjoo Oh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

We present SYMTUNER, a novel technique to automatically tune external parameters of symbolic execution. Practical symbolic execution tools have important external parameters (e.g., symbolic arguments, seed input) that critically affect their performance. Due to the huge parameter space, however, manually customizing those parameters is notoriously difficult even for experts. As a consequence, symbolic execution tools have typically been used in a suboptimal manner that, for example, simply relies on the default parameter settings of the tools and loses the opportunity for better performance. In this paper, we aim to change this situation by automatically configuring symbolic execution parameters. With Symtuner that takes parameter spaces to be tuned, symbolic executors are run without manual parameter configurations; instead, appropriate parameter values are learned and adjusted during symbolic execution. To achieve this, we present a learning algorithm that observes the behavior of symbolic execution and accordingly updates the sampling probability of each parameter space. We evaluated Symtuner with KLEE on 12 open-source C programs. The results show that Symtuner increases branch coverage of KLEE by 56% on average and finds 8 more bugs than KLEE with its default parameters over the latest releases of the programs.

Original languageEnglish
Title of host publicationProceedings - 2022 ACM/IEEE 44th International Conference on Software Engineering, ICSE 2022
PublisherIEEE Computer Society
Pages2068-2079
Number of pages12
ISBN (Electronic)9781450392211
DOIs
Publication statusPublished - 2022
Event44th ACM/IEEE International Conference on Software Engineering, ICSE 2022 - Pittsburgh, United States
Duration: 2022 May 222022 May 27

Publication series

NameProceedings - International Conference on Software Engineering
Volume2022-May
ISSN (Print)0270-5257

Conference

Conference44th ACM/IEEE International Conference on Software Engineering, ICSE 2022
Country/TerritoryUnited States
CityPittsburgh
Period22/5/2222/5/27

Bibliographical note

Publisher Copyright:
© 2022 ACM.

Keywords

  • Software Testing
  • Symbolic Execution

ASJC Scopus subject areas

  • Software

Fingerprint

Dive into the research topics of 'SYMTUNER: Maximizing the Power of Symbolic Execution by Adaptively Tuning External Parameters'. Together they form a unique fingerprint.

Cite this