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LLMs and fuzzing in tandem: a new approach to automatically generating weakest preconditions.

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Title: LLMs and fuzzing in tandem: a new approach to automatically generating weakest preconditions.
Authors: King, Daragh1 (AUTHOR) kingd6@tcd.ie, Koutavas, Vasileios1 (AUTHOR) vasileios.koutavas@tcd.ie, Kovács, Laura2 (AUTHOR) laura.kovacs@tuwien.ac.at
Source: International Journal on Software Tools for Technology Transfer. Jun2026, Vol. 28 Issue 3, p317-328. 12p.
Subjects: Software verification, Computer software testing, Java programming language, Language models, Computer software
Abstract: The weakest precondition (WP) of a program describes the largest set of initial states from which all terminating executions of the program satisfy a given postcondition. The generation of WPs is an important task with practical applications in areas ranging from verification to run-time error checking. This paper proposes the combination of Large Language Models (LLMs) and fuzz testing for generating WPs. In pursuit of this goal, we introduce Fuzzing Guidance (FG); FG acts as a means of directing LLMs towards correct WPs using program execution feedback. FG utilises fuzz testing for approximately checking the validity and weakness of candidate WPs, this information is then fed back to the LLM as a means of context refinement. We demonstrate the effectiveness of our approach on a comprehensive benchmark set of deterministic array programs in Java. Our experiments indicate that LLMs are capable of producing viable candidate WPs, and that this ability can be practically enhanced through FG. [ABSTRACT FROM AUTHOR]
Copyright of International Journal on Software Tools for Technology Transfer is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 194640828
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PubTypeId: academicJournal
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+on+Software+Tools+for+Technology+Transfer%22">International Journal on Software Tools for Technology Transfer</searchLink>. Jun2026, Vol. 28 Issue 3, p317-328. 12p.
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  Data: The weakest precondition (WP) of a program describes the largest set of initial states from which all terminating executions of the program satisfy a given postcondition. The generation of WPs is an important task with practical applications in areas ranging from verification to run-time error checking. This paper proposes the combination of Large Language Models (LLMs) and fuzz testing for generating WPs. In pursuit of this goal, we introduce Fuzzing Guidance (FG); FG acts as a means of directing LLMs towards correct WPs using program execution feedback. FG utilises fuzz testing for approximately checking the validity and weakness of candidate WPs, this information is then fed back to the LLM as a means of context refinement. We demonstrate the effectiveness of our approach on a comprehensive benchmark set of deterministic array programs in Java. Our experiments indicate that LLMs are capable of producing viable candidate WPs, and that this ability can be practically enhanced through FG. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of International Journal on Software Tools for Technology Transfer is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s10009-026-00844-2
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        Type: general
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      – SubjectFull: Java programming language
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      – SubjectFull: Computer software
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              M: 06
              Text: Jun2026
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              Y: 2026
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