Effective incident management is pivotal for the smooth operation of
enterprises-level cloud services. In order to expedite incident mitigation,
service teams compile troubleshooting knowledge into Troubleshooting Guides
(TSGs) accessible to on-call engineers (OCEs). While automated pipelines are
enabled to resolve the most frequent and easy incidents, there still exist
complex incidents that require OCEs' intervention. However, TSGs are often
unstructured and incomplete, which requires manual interpretation by OCEs,
leading to on-call fatigue and decreased productivity, especially among
new-hire OCEs. In this work, we propose Nissist which leverages TSGs and
incident mitigation histories to provide proactive suggestions, reducing human
intervention. Leveraging Large Language Models (LLM), Nissist extracts insights
from unstructured TSGs and historical incident mitigation discussions, forming
a comprehensive knowledge base. Its multi-agent system design enhances
proficiency in precisely discerning user queries, retrieving relevant
information, and delivering systematic plans consecutively. Through our user
case and experiment, we demonstrate that Nissist significant reduce Time to
Mitigate (TTM) in incident mitigation, alleviating operational burdens on OCEs
and improving service reliability. Our demo is available at
this https URL

Nissist 通过利用故障排除指南和历史事故缓解讨论，提供主动建议，减少人工干预，从而显著缩短事故缓解时间（TTM）并提高服务可靠性。

Nissist: 基于故障处理指南的事件缓解副驾驶

Nissist: An Incident Mitigation Copilot based on Troubleshooting Guides

Incident management is a key aspect of operating large-scale cloud services.
To aid with faster and efficient resolution of incidents, engineering teams
document frequent troubleshooting steps in the form of Troubleshooting Guides
(TSGs), to be used by on-call engineers (OCEs). However, TSGs are siloed,
unstructured, and often incomplete, requiring developers to manually understand
and execute necessary steps. This results in a plethora of issues such as
on-call fatigue, reduced productivity, and human errors. In this work, we
conduct a large-scale empirical study of over 4K+ TSGs mapped to 1000s of
incidents and find that TSGs are widely used and help significantly reduce
mitigation efforts. We then analyze feedback on TSGs provided by 400+ OCEs and
propose a taxonomy of issues that highlights significant gaps in TSG quality.
To alleviate these gaps, we investigate the automation of TSGs and propose
AutoTSG -- a novel framework for automation of TSGs to executable workflows by
combining machine learning and program synthesis. Our evaluation of AutoTSG on
50 TSGs shows the effectiveness in both identifying TSG statements (accuracy
0.89) and parsing them for execution (precision 0.94 and recall 0.91). Lastly,
we survey ten Microsoft engineers and show the importance of TSG automation and
the usefulness of AutoTSG.

本研究通过大规模实证研究发现疏通指南 (TSGs) 被广泛使用，帮助减少云服务故障的缓解工作，但常常存在结构不明、内容不完整的问题，因此本文提出利用机器学习和程序合成自动化 TSGs 的 AutoTSG 框架并进行试验评估，结果显示其准确性达到 0.89，同时具有很高的精度和召回率。