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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]
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Database: Engineering Source
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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]
ISSN:14332779
DOI:10.1007/s10009-026-00844-2