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    This study introduces a novel Large Language Model (LLM) induction method to overcome path explosion in symbolic execution for healthcare software. This approach significantly speeds up vulnerability detection, enhancing patient safety and software reliability.

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    Area of Science:

    • Computer Science
    • Software Engineering
    • Artificial Intelligence

    Background:

    • Healthcare software accuracy is critical for patient safety and high-quality care.
    • Software reliability is paramount in healthcare systems.
    • Symbolic execution is vital for automated vulnerability detection but suffers from path explosion, hindering efficiency.

    Purpose of the Study:

    • To propose an efficient method for automated vulnerability detection in healthcare software.
    • To address the path explosion problem in symbolic execution.
    • To enhance the scalability and effectiveness of software defect verification.

    Main Methods:

    • Integration of Natural Language Processing (NLP) and Generative Pre-trained Transformer (GPT) models.
    • Development of a Large Language Model (LLM) induction method to mitigate path explosion in symbolic execution.
    • Application of the proposed method to symbolic execution engines for vulnerability detection.

    Main Results:

    • The LLM induction method detects vulnerabilities in seconds, outperforming traditional symbolic execution engines that often time out or run out of memory.
    • The proposed approach significantly improves the scalability of symbolic execution.
    • Complex programs can be analyzed with minimal increases in computational resources or time.

    Conclusions:

    • The LLM induction method offers a more efficient and scalable solution for automated vulnerability detection in healthcare software.
    • This advancement is crucial for improving the reliability and security of modern healthcare systems.
    • The method enhances the overall effectiveness of automated defect verification, ensuring patient safety and trust.