  Unit testing is a commonly-used approach in software engineering to test the
correctness and robustness of written code. Unit tests are tests designed to
test small components of a codebase in isolation, such as an individual
function or method. Although unit tests have historically been written by human
programmers, recent advancements in AI, particularly LLMs, have shown
corresponding advances in automatic unit test generation. In this study, we
explore the effect of different prompts on the quality of unit tests generated
by Code Interpreter, a GPT-4-based LLM, on Python functions provided by the
Quixbugs dataset, and we focus on prompting due to the ease with which users
can make use of our findings and observations. We find that the quality of the
generated unit tests is not sensitive to changes in minor details in the
prompts provided. However, we observe that Code Interpreter is often able to
effectively identify and correct mistakes in code that it writes, suggesting
that providing it runnable code to check the correctness of its outputs would
be beneficial, even though we find that it is already often able to generate
correctly-formatted unit tests. Our findings suggest that, when prompting
models similar to Code Interpreter, it is important to include the basic
information necessary to generate unit tests, but minor details are not as
important.
