
awesome-LLM-AIOps
A list of awesome academic researches and industrial materials about Large Language Model (LLM) and Artificial Intelligence for IT Operations (AIOps).
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The 'awesome-LLM-AIOps' repository is a curated list of academic research and industrial materials related to Large Language Models (LLM) and Artificial Intelligence for IT Operations (AIOps). It covers various topics such as incident management, log analysis, root cause analysis, incident mitigation, and incident postmortem analysis. The repository provides a comprehensive collection of papers, projects, and tools related to the application of LLM and AI in IT operations, offering valuable insights and resources for researchers and practitioners in the field.
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A list of awesome academic researches and industrial materials about Large Language Model (LLM) and Artificial Intelligence for IT Operations (AIOps).
- Awesome LLM AIOps
This is a list of awesome academic researches and industrial materials about Large Language Model (LLM) and Artificial Intelligence for IT Operations (AIOps).
The utilized LLM techniques used in the work.
The mainly explored task of the work.
Other important information of the work.
- [HotNets 2023] A Holistic View of AI-driven Network Incident Management.
- [ESEC/FSE Industry 2023] Assess and Summarize: Improve Outage Understanding with Large Language Models.
- [ICSE-SEIP 2024] Knowledge-aware Alert Aggregation in Large-scale Cloud Systems: a Hybrid Approach.
- [FSE Industry 2024] MonitorAssistant: Simplifying Cloud Service Monitoring via Large Language Models.
- [ICSE 2024] Xpert: Empowering Incident Management with Query Recommendations via Large Language Models.
- [ICSE 2023] Recommending Root-Cause and Mitigation Steps for Cloud Incidents using Large Language Models.
- [FSE Industry 2024] LM-PACE: Confidence Estimation by Large Language Models for Effective Root Causing of Cloud Incidents.
- [FSE Industry 2024] Automated Root Causing of Cloud Incidents using In-Context Learning with GPT-4.
- [FSE Industry 2024] X-lifecycle Learning for Cloud Incident Management using LLMs.
- [FSE Industry 2024] Exploring LLM-based Agents for Root Cause Analysis.
- [Preprint 2023] D-Bot: Database Diagnosis System using Large Language Models [project].
- [Preprint 2023] RCAgent: Cloud Root Cause Analysis by Autonomous Agents with Tool-Augmented Large Language Models.
- [Preprint 2024] mABC: Multi-Agent Blockchain-inspired Collaboration for Root Cause Analysis in Micro-Services Architecture.
- [FSE Industry 2024] Leveraging Large Language Models for the Auto-remediation of Microservice Applications - An Experimental Study.
- [ECAI 2024] Nissist: An Incident Mitigation Copilot based on Troubleshooting Guides.
- [SIGOPS 2024] LLexus: an AI agent system for incident management.
- [ICSE-SEIP 2024] FaultProfIT: Hierarchical Fault Profiling of Incident Tickets in Large-scale Cloud Systems.
- [Preprint 2024] FAIL: Analyzing Software Failures from the News Using LLMs.
- [EMNLP Industry 2023] Empower Large Language Model to Perform Better on Industrial Domain-Specific Question Answering [project].
- [ICLR 2024] OWL: A Large Language Model for IT Operations [project].
- [SANER 2024] Gloss: Guiding Large Language Models to Answer Questions from System Logs.
- [Preprint 2023] An Empirical Study of NetOps Capability of Pre-Trained Large Language Models [project].
- [Preprint 2023] OpsEval: A Comprehensive Task-Oriented AIOps Benchmark for Large Language Models [project].
- [ISSTA 2024] A Large-Scale Evaluation for Log Parsing Techniques: How Far Are We?.
- [ASE-NIER 2023] Log Parsing: How Far Can ChatGPT Go? [project].
- [ICSE 2024] DivLog: Log Parsing with Prompt Enhanced In-Context Learning.
- [ICSE 2024] LLMParser: An Exploratory Study on Using Large Language Models for Log Parsing.
- [FSE 2024] LILAC: Log Parsing using LLMs with Adaptive Parsing Cache.
- [ICPC 2024] Interpretable Online Log Analysis Using Large Language Models with Prompt Strategies.
- [Preprint 2023] LEMUR : Log Parsing with Entropy Sampling and Chain-of-Thought Merging.
- [Preprint 2024] Stronger, Cheaper and Demonstration-Free Log Parsing with LLMs.
- [Preprint 2024] LUNAR: Unsupervised LLM-based Log Parsing.
- [Preprint 2024] Log Parsing with Self-Generated In-Context Learning and Self-Correction.
- [Preprint 2024] OpenLogParser: Unsupervised Parsing with Open-Source Large Language Models.
- [ICPC 2024] Interpretable Online Log Analysis Using Large Language Models with Prompt Strategies.
- [Preprint 2023] Log-based Anomaly Detection based on EVT Theory with feedback.
- [Preprint 2023] LogGPT: Exploring ChatGPT for Log-Based Anomaly Detection.
- [Preprint 2024] RAGLog: Log Anomaly Detection using Retrieval Augmented Generation.
- [Preprint 2024] Anomaly Detection on Unstable Logs with GPT Models.
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[ICSE 2024] UniLog: Automatic Logging via LLM and In-Context Learning.
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[FSE 2024] Go Static: Contextualized Logging Statement Generation.
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[Preprint 2023] Exploring the Effectiveness of LLMs in Automated Logging Generation: An Empirical Study [project].
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- Then, add keywords tags. Add the pdf link of the paper. If it is an arxiv publication, we prefer /abs/ format to /pdf/ format.
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