A Large Language Model (LLM) represents a cutting-edge artificial intelligence model that generates coherent content, including grammatically precise sentences, human-like paragraphs, and syntactically accurate code snippets. LLMs can play a pivotal role in software development, including software testing. LLMs go beyond traditional roles such as requirement analysis and documentation and can support test case generation, making them valuable tools that significantly enhance testing practices within the field. Hence, we explore the practical application of LLMs in software testing within an industrial setting, focusing on their current use by professional testers. In this context, rather than relying on existing data, we conducted a cross-sectional survey and collected data within real working contexts, specifically, engaging with practitioners in industrial settings. We applied quantitative and qualitative techniques to analyze and synthesize our collected data. Our findings demonstrate that LLMs effectively enhance testing documents and significantly assist testing professionals in programming tasks like debugging and test case automation. LLMs can support individuals engaged in manual testing who need to code. However, it is crucial to emphasize that, at this early stage, software testing professionals should use LLMs with caution while well-defined methods and guidelines are being built for the secure adoption of these tools.

翻译：大语言模型（LLM）是一种前沿的人工智能模型，能够生成连贯的内容，包括语法精确的句子、类似人类的段落以及语法正确的代码片段。LLM在软件开发（包括软件测试）中可发挥关键作用。LLM超越了需求分析和文档编写等传统角色，还能支持测试用例生成，成为显著提升软件测试实践的重要工具。因此，我们探索了LLM在工业环境中软件测试的实际应用，重点关注专业测试人员当前的使用情况。在此背景下，我们未依赖现有数据，而是开展了一项横截面研究，在真实工作环境中收集数据，特别地，与工业领域的从业者进行了互动。我们采用定量和定性技术对所收集的数据进行分析和综合。研究结果表明，LLM能有效增强测试文档，并在调试和测试用例自动化等编程任务中显著帮助测试专业人员。LLM能够支持需要编写代码的从事手动测试的人员。然而，必须强调的是，在目前这一早期阶段，软件测试专业人员应谨慎使用LLM，同时正在为安全采用这些工具建立明确的方法和指南。