标题： 利用大语言模型对OpenCV库进行文档引导的模糊测试 显示英文标题

标题： Harnessing LLMs for Document-Guided Fuzzing of OpenCV Library

摘要： 计算机视觉和人工智能的结合正在通过使机器能够以高精度解释和处理视觉数据，从根本上改变广泛的行业。 作为最大且目前最受欢迎的开源计算机视觉库，OpenCV库提供了一套全面的编程函数，支持实时计算机视觉。 OpenCV库中的错误可能会影响下游的计算机视觉应用，确保OpenCV库的可靠性至关重要。 本文介绍了VISTAFUZZ，一种利用大型语言模型（LLMs）对OpenCV库进行文档引导模糊测试的新技术。 VISTAFUZZ利用LLMs解析API文档并获取标准化的API信息。 基于这些标准化信息，VISTAFUZZ提取单个输入参数上的约束条件以及这些参数之间的依赖关系。 使用这些约束和依赖关系，VISTAFUZZ然后生成新的输入值以系统地测试每个目标API。 我们评估了VISTAFUZZ在测试OpenCV库中330个API时的有效性，结果表明VISTAFUZZ检测到了17个新错误，其中10个错误已被确认，其中有5个已修复。 显示英文摘要

摘要： The combination of computer vision and artificial intelligence is fundamentally transforming a broad spectrum of industries by enabling machines to interpret and act upon visual data with high levels of accuracy. As the biggest and by far the most popular open-source computer vision library, OpenCV library provides an extensive suite of programming functions supporting real-time computer vision. Bugs in the OpenCV library can affect the downstream computer vision applications, and it is critical to ensure the reliability of the OpenCV library. This paper introduces VISTAFUZZ, a novel technique for harnessing large language models (LLMs) for document-guided fuzzing of the OpenCV library. VISTAFUZZ utilizes LLMs to parse API documentation and obtain standardized API information. Based on this standardized information, VISTAFUZZ extracts constraints on individual input parameters and dependencies between these. Using these constraints and dependencies, VISTAFUZZ then generates new input values to systematically test each target API. We evaluate the effectiveness of VISTAFUZZ in testing 330 APIs in the OpenCV library, and the results show that VISTAFUZZ detected 17 new bugs, where 10 bugs have been confirmed, and 5 of these have been fixed.