Recommending Root-Cause and Mitigation Steps for Cloud Incidents using Large Language Models-Reference-Cited by-同舟云学术

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Recommending Root-Cause and Mitigation Steps for Cloud Incidents using Large Language Models

Published:2023-05 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE)
language:
Short-container-title:

Author:

Ahmed Toufique¹,Ghosh Supriyo²,Bansal Chetan²,Zimmermann Thomas³,Zhang Xuchao²,Rajmohan Saravan²

Affiliation:

1. UC Davis

2. Microsoft

3. Microsoft Research

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10172484/10172342/10172904.pdf?arnumber=10172904

Reference65 articles.

1. CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation

2. Picking Pearl from Seabed: Extracting Artefacts from Noisy Issue Triaging Collaborative Conversations for Hybrid Cloud Services

3. Continuous Incident Triage for Large-Scale Online Service Systems

4. Long Short-Term Memory

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1. Variability Management for Large Language Model Tasks: Practical Insights from an Industrial Application;28th ACM International Systems and Software Product Line Conference;2024-09-02

2. LLexus: an AI agent system for incident management;ACM SIGOPS Operating Systems Review;2024-08-14

3. X-Lifecycle Learning for Cloud Incident Management using LLMs;Companion Proceedings of the 32nd ACM International Conference on the Foundations of Software Engineering;2024-07-10

4. LM-PACE: Confidence Estimation by Large Language Models for Effective Root Causing of Cloud Incidents;Companion Proceedings of the 32nd ACM International Conference on the Foundations of Software Engineering;2024-07-10

5. Automated Root Causing of Cloud Incidents using In-Context Learning with GPT-4;Companion Proceedings of the 32nd ACM International Conference on the Foundations of Software Engineering;2024-07-10

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