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.

翻译：在软件工程中，软件工具的细致配置对于确保其在复杂系统中的最优性能至关重要。然而，现代应用呈现的高维搜索空间加剧了选择最优配置的内在复杂性。传统的试错法或直觉驱动方法既低效又易出错，阻碍了可扩展性与可复现性。本研究探索利用大型语言模型（LLM）来简化软件配置过程。我们识别出智能应用中机器学习组件的超参数配置任务尤为困难，因其面临广泛的搜索空间和关键的性能需求。现有方法（包括贝叶斯优化）在初始化设置、计算成本和收敛效率方面存在局限。我们提出了一种新方法，利用Chat-GPT等LLM来识别起始条件并缩小搜索空间，从而提高配置效率。通过一系列实验研究LLM生成响应的变异性，我们发现了有趣的现象，例如潜在的响应缓存机制及基于领域特定关键词的一致行为模式。此外，超参数优化实验的结果揭示了LLM在加速初始化进程和优化配置方面的潜力。尽管初步发现令人鼓舞，但也表明需要在该领域进行更深入的探索与实验。