Xpert: Empowering Incident Management with Query Recommendations via Large Language Models
Abstract
Large-scale cloud systems play a pivotal role in modern IT infrastructure. However, incidents occurring within these systems can lead to service disruptions and adversely affect user experience. To swiftly resolve such incidents, on-call engineers depend on crafting domain-specific language (DSL) queries to analyze telemetry data. However, writing these queries can be challenging and time-consuming. This paper presents a thorough empirical study on the utilization of queries of KQL, a DSL employed for incident management in a large-scale cloud management system at Microsoft. The findings obtained underscore the importance and viability of KQL queries recommendation to enhance incident management.
BibTeX
@inproceedings{Jiang-al:ICSE24,
author = {Yuxuan Jiang and
Chaoyun Zhang and
Shilin He and
Zhihao Yang and
Minghua Ma and
Si Qin and
Yu Kang and
Yingnong Dang and
Saravan Rajmohan and
Qingwei Lin and
Dongmei Zhang},
title = {Xpert: Empowering Incident Management with Query Recommendations via Large Language Models},
booktitle = {ICSE},
pages = {92:1--92:13},
publisher = {{ACM}},
year = {2024},
}