This repository is a curated list of academic research and industrial materials focused on applying Large Language Models (LLMs) to Artificial Intelligence for IT Operations (AIOps). It serves as a valuable resource for researchers and practitioners seeking to leverage LLMs for tasks like incident management, log analysis, and root cause analysis in IT systems.

How It Works

The list categorizes research based on specific AIOps tasks, including incident lifecycle management (reporting, diagnosis, root cause analysis, mitigation, postmortem) and log analysis (parsing, anomaly detection, logging statement generation). Each entry typically includes a brief description, keywords indicating the LLM techniques and tasks explored, and links to publications or projects.

Quick Start & Requirements

This is a curated list, not a software package. No installation or execution is required. The content points to external academic papers and projects.

Highlighted Details

• Comprehensive coverage of LLM applications across the incident management lifecycle.
• Detailed sections on LLM-based log parsing, anomaly detection, and logging statement generation.
• Includes both academic preprints and peer-reviewed publications from major conferences (ICSE, ESEC/FSE, VLDB, etc.).
• Provides links to projects and benchmarks for practical evaluation.

Maintenance & Community

The repository encourages community contributions to expand and refine the list. Instructions are provided for submitting new research.

Licensing & Compatibility

The repository itself is likely under a permissive license (e.g., MIT, Apache 2.0) as it's a list of links. However, the licensing of the linked academic papers and projects will vary and must be checked individually.

Limitations & Caveats

This is a reference list and does not provide executable code or implementations. The rapid evolution of LLMs means the list may require frequent updates to remain comprehensive.