  The integration of Large Language Models (LLMs) and chatbots introduces new
challenges and opportunities for decision-making in software testing.
Decision-making relies on a variety of information, including code,
requirements specifications, and other software artifacts that are often
unclear or exist solely in the developer's mind. To fill in the gaps left by
unclear information, we often rely on assumptions, intuition, or previous
experiences to make decisions. This paper explores the potential of LLM-based
chatbots like Bard, Copilot, and ChatGPT, to support software testers in test
decisions such as prioritizing test cases effectively. We investigate whether
LLM-based chatbots and human testers share similar "assumptions" or intuition
in prohibitive testing scenarios where exhaustive execution of test cases is
often impractical. Preliminary results from a survey of 127 testers indicate a
preference for diverse test scenarios, with a significant majority (96%)
favoring dissimilar test sets. Interestingly, two out of four chatbots mirrored
this preference, aligning with human intuition, while the others opted for
similar test scenarios, chosen by only 3.9% of testers. Our initial insights
suggest a promising avenue within the context of enhancing the collaborative
dynamics between testers and chatbots.
