SysResolve: Study on In-Context LLM Generation of Resolution Scripts
Abstract
Microservices pose challenges for automated fault resolution due to their distributed and complex nature. We present SysResolve, a framework that automates the entire resolution pipeline by combining multi-modal Root Cause Analysis (RCA) with Large Language Models (LLMs). RCA outputs are converted to natural language and passed through a Retrieval-Augmented Generation (RAG) pipeline to produce executable scripts. We evaluated and experimented on two microservices applications with three LLM (LlaMa3-70B, GPT-4, Claude 3.7). Our analysis highlights significant gains of current LLMs generation power from few-shot learning, with SysResolve achieving expert-level remediation while reducing recovery time.
Contributors
Bibtex
@inproceedings{borse2025sysresolve,
title={SysResolve: Study on In-Context LLM Generation of Resolution Scripts},
author={Borse, Harsh and Satpathy, Utkalika and Mondal, Mainack and Mitra, Bivas},
booktitle={International Conference on Database and Expert Systems Applications},
pages={130--136},
year={2025},
organization={Springer}
}
Cite
Borse, H., Satpathy, U., Mondal, M., & Mitra, B. (2025, August). SysResolve: Study on In-Context LLM Generation of Resolution Scripts. In International Conference on Database and Expert Systems Applications (pp. 130-136). Cham: Springer Nature Switzerland.