  In software engineering, the meticulous configuration of software tools is
crucial in ensuring optimal performance within intricate systems. However, the
complexity inherent in selecting optimal configurations is exacerbated by the
high-dimensional search spaces presented in modern applications. Conventional
trial-and-error or intuition-driven methods are both inefficient and
error-prone, impeding scalability and reproducibility. In this study, we embark
on an exploration of leveraging Large-Language Models (LLMs) to streamline the
software configuration process. We identify that the task of hyperparameter
configuration for machine learning components within intelligent applications
is particularly challenging due to the extensive search space and
performance-critical nature. Existing methods, including Bayesian optimization,
have limitations regarding initial setup, computational cost, and convergence
efficiency. Our work presents a novel approach that employs LLMs, such as
Chat-GPT, to identify starting conditions and narrow down the search space,
improving configuration efficiency. We conducted a series of experiments to
investigate the variability of LLM-generated responses, uncovering intriguing
findings such as potential response caching and consistent behavior based on
domain-specific keywords. Furthermore, our results from hyperparameter
optimization experiments reveal the potential of LLMs in expediting
initialization processes and optimizing configurations. While our initial
insights are promising, they also indicate the need for further in-depth
investigations and experiments in this domain.
