KLEE symbolic execution engine in 2019
File(s)
Author(s)
Cadar, Cristian
Nowack, Martin
Type
Journal Article
Abstract
KLEE is a popular dynamic symbolic execution engine, initially designed at Stanford University and now primarily developed and maintained by the Software Reliability Group at Imperial College London. KLEE has a large community spanning both academia and industry, with over 60 contributors on GitHub, over 350 subscribers on its mailing list, and over 80 participants to a recent dedicated workshop. KLEE has been used and extended by groups from many universities and companies in a variety of different areas such as high-coverage test generation, automated debugging, exploit generation, wireless sensor networks, and online gaming, among many others.
Date Issued
2020-06-02
Date Acceptance
2020-06-01
Citation
International Journal on Software Tools for Technology Transfer, 2020, 23, pp.867-870
ISSN
0945-8115
Publisher
Springer
Start Page
867
End Page
870
Journal / Book Title
International Journal on Software Tools for Technology Transfer
Volume
23
Copyright Statement
© The Author(s) 2020. This article is licensed under a Creative Commons
Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as
long as you give appropriate credit to the original author(s) and the
source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material
in this article are included in the article’s Creative Commons licence,
unless indicated otherwise in a credit line to the material. If material
is not included in the article’s Creative Commons licence and your
intended use is not permitted by statutory regulation or exceeds the
permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecomm
ons.org/licenses/by/4.0
Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as
long as you give appropriate credit to the original author(s) and the
source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material
in this article are included in the article’s Creative Commons licence,
unless indicated otherwise in a credit line to the material. If material
is not included in the article’s Creative Commons licence and your
intended use is not permitted by statutory regulation or exceeds the
permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecomm
ons.org/licenses/by/4.0
License URL
Identifier
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000537357300001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Subjects
Science & Technology
Technology
Computer Science, Software Engineering
Computer Science
Dynamic symbolic execution
Bug finding
Test generation
Publication Status
Published online
Date Publish Online
2020-06-02