Exploring the Potential of Large Language Models in Self-adaptive Systems

被引：2

作者：

Li, Jialong ^{[1
]}

Zhang, Mingyue ^{[2
]}

Li, Nianyu ^{[3
]}

Weyns, Danny ^{[4
]}

Jin, Zhi ^{[5
]}

Tei, Kenji ^{[6
]}

机构：

[1] Waseda Univ, Tokyo, Japan

[2] Southwest Univ, Chongqing, Peoples R China

[3] ZGC Natl Lab, Beijing, Peoples R China

[4] Katholieke Univ Leuven, Leuven, Belgium

[5] Peking Univ, Beijing, Peoples R China

[6] Tokyo Inst Technol, Tokyo, Japan

来源：

PROCEEDINGS OF THE 2024 IEEE/ACM 19TH SYMPOSIUM ON SOFTWARE ENGINEERING FOR ADAPTIVE AND SELF-MANAGING SYSTEMS, SEAMS 2024 | 2024年

关键词：

Large Language Model; Self-adaptive Systems; Survey;

D O I：

10.1145/3643915.3644088

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Large Language Models (LLMs), with their abilities in knowledge acquisition and reasoning, can potentially enhance the various aspects of Self-adaptive Systems (SAS). Yet, the potential of LLMs in SAS remains largely unexplored and ambiguous, due to the lack of literature from flagship conferences or journals in the field, such as SEAMS and TAAS. The interdisciplinary nature of SAS suggests that drawing and integrating ideas from related fields, such as software engineering and autonomous agents, could unveil innovative research directions for LLMs within SAS. To this end, this paper reports the results of a literature review of studies in relevant fields, summarizes and classifies the studies relevant to SAS, and outlines their potential to specific aspects of SAS. Literature classification: www.github.com/545659928/LLM4SAS

引用

页码：77 / 83

页数：7

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