mutest-rs: Flexible, efficient mutation analysis tool for rust programs, using extensive static analysis

Lévai, Z. orcid.org/0009-0003-4173-8562, Shin, D. orcid.org/0000-0002-0840-6449 and McMinn, P. orcid.org/0000-0001-9137-7433 (2026) mutest-rs: Flexible, efficient mutation analysis tool for rust programs, using extensive static analysis. In: 2026 IEEE International Conference on Software Testing, Verification and Validation (ICST). 2026 IEEE International Conference on Software Testing, Verification and Validation (ICST), 18-22 May 2026, Daejeon, Korea, Republic of. . Institute of Electrical and Electronics Engineers (IEEE), pp. 216-220. ISBN: 9798319533098. ISSN: 2159-4848.

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Item Type: Proceedings Paper
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© 2026 The Authors. Except as otherwise noted, this author-accepted version of a proceedings paper published in 2026 IEEE International Conference on Software Testing, Verification and Validation (ICST) is made available via the University of Sheffield Research Publications and Copyright Policy under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/

Keywords: Testing; Programming; Codes; Timing; Tools; Program processors; Radio access networks; Regional area networks; Runtime; Printing
Dates:
  • Published (online): 16 July 2026
  • Published: 16 July 2026
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield)
Funding Information:
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Grant number
ENGINEERING AND PHYSICAL SCIENCE RESEARCH COUNCIL
EP/X024539/1
ENGINEERING AND PHYSICAL SCIENCE RESEARCH COUNCIL
EP/Y014219/1
Date Deposited: 10 Aug 2026 15:59
Last Modified: 10 Aug 2026 16:07
Status: Published
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Refereed: Yes
Identification Number: 10.1109/icst69053.2026.00038
Open Archives Initiative ID (OAI ID):

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