  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.
