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HoarePrompt uses program verification to improve LLM-based software correctness analysis

By PulseAugur Editorial · [1 sources] ·

Researchers have developed HoarePrompt, a novel method that integrates program verification principles with natural language processing to assess software correctness. This approach adapts the strongest postcondition calculus and uses a few-shot-driven k-induction technique to manage loops, enabling large language models to systematically describe program states. HoarePrompt was evaluated on the CoCoClaNeL dataset, demonstrating significant improvements in correctness classification compared to standard zero-shot CoT prompts and LLM-based test generation. AI

IMPACT Enhances LLM capabilities in formal software verification, potentially improving code quality and reliability.

RANK_REASON The cluster contains an academic paper detailing a new method for program correctness verification using LLMs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

HoarePrompt uses program verification to improve LLM-based software correctness analysis

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The cluster contains an academic paper detailing a new method for program correctness verification using LLMs. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Dimitrios Stamatios Bouras, Yihan Dai, Tairan Wang, Yingfei Xiong, Sergey Mechtaev ·

    HoarePrompt: Structural Reasoning About Program Correctness in Natural Language

    arXiv:2503.19599v3 Announce Type: replace-cross Abstract: While software requirements are often expressed in natural language, verifying the correctness of a program against such requirements is a hard and underexplored problem. Large language models (LLMs) are promising candidat…

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