1 citations · 2 across the 15 of their papers we have counts for
18 papers
Rethinking Tool Design for Agentic RCA: A Controlled Empirical Study
Yu Luo, Rongchen Gao, Zhenhui Zhou +7
Large language model (LLM) agents are increasingly explored for root cause analysis (RCA) in microservice systems, yet empirical guidance on how to design and combine their tools r…
From Noisy Telemetry to Actionable Warnings: GPU Failure Prediction in Industrial Clusters
Yongqian Sun, Run Zhu, Wenwei Gu +9
GPU clusters are critical infrastructure for AI services, but accurate and actionable GPU failure prediction remains a problem in production settings. We study ticket-linked teleme…
KRCA: An Efficient Root Cause Analysis System in Hyper-scale Microservice Systems via Agentic AI
Jiamin Jiang, Jingfei Feng, Yu Luo +9
Hyper-scale microservice systems have become the standard infrastructure for large-scale Internet companies. These systems consist of numerous loosely coupled microservices that ev…
OpsMem: Dual-Memory Reasoning with Cross-Memory Resonance for Failure Diagnosis
Yongqian Sun, Rongchen Gao, Yu Luo +6
Failure diagnosis in modern software systems requires iterative evidence acquisition and hypothesis reasoning guided by operational experience. Existing LLM-based methods improve d…
AgentTether: Graph-Guided Diagnosis and Runtime Intervention for Reliable LLM Agent Operation
Chenyu Zhao, Shenglin Zhang, Wenwei Gu +5
Large language model (LLM) agents are increasingly used for multi-step, stateful tool-use tasks, yet production reliability remains limited. Unlike static software repair, agent re…
A Multi-Dataset Benchmark for Evaluating LLM Agents in Microservice Failure Diagnosis
Yuanhong Cai, Xiaohui Nie, Kanglin Yin +8
LLM-based agents are reshaping microservice operations into AgentOps, where benchmarks are key to evaluating failure diagnosis over multimodal observability data. However, existing…
Debugging the Debuggers: Failure-Anchored Structured Recovery for Software Engineering Agents
Chenyu Zhao, Shenglin Zhang, Yihang Lin +7
Software engineering agents are increasingly deployed in evaluable engineering environments, yet post-failure recovery remains costly, manual, and ad hoc. Existing systems expose t…
EvidenT: An Evidence-Preserving Framework for Iterative System-Level Package Repair
Chenyu Zhao, Minghua Ma, Shenglin Zhang +5
Frequent toolchain updates and growing ISA diversity have made system-level software package repair increasingly important. Diagnosing and repairing build failures remains challeng…
Graph of States: Solving Abductive Tasks with Large Language Models
Yu Luo, Rongchen Gao, Lu Teng +9
Logical reasoning encompasses deduction, induction, and abduction. However, while Large Language Models (LLMs) have effectively mastered the former two, abductive reasoning remains…
A Benchmark for Language Models in Real-World System Building
Weilin Jin, Chenyu Zhao, Zeshun Huang +12
During migration across instruction set architectures (ISAs), software package build repair is a critical task for ensuring the reliability of software deployment and the stability…
Triage in Software Engineering: A Systematic Review of Research and Practice
Yongxin Zhao, Shenglin Zhang, Yujia Wu +5
As modern software systems continue to grow in complexity, triage has become a fundamental process in system operations and maintenance. Triage aims to efficiently prioritize, assi…
Can Language Models Go Beyond Coding? Assessing the Capability of Language Models to Build Real-World Systems
Chenyu Zhao, Shenglin Zhang, Zeshun Huang +8
Large language models (LLMs) have shown growing potential in software engineering, yet few benchmarks evaluate their ability to repair software during migration across instruction…
OpsAgent: An Evolving Multi-agent System for Incident Management in Microservices
Yu Luo, Jiamin Jiang, Jingfei Feng +6
Incident management (IM) is central to the reliability of large-scale microservice systems. Yet manual IM, where on-call engineers examine metrics, logs, and traces is labor-intens…
R-Log: Incentivizing Log Analysis Capability in LLMs via Reasoning-based Reinforcement Learning
Yilun Liu, Ziang Chen, Song Xu +10
The growing complexity of log data in modern software systems has prompted the use of Large Language Models (LLMs) for automated log analysis. Current approaches typically rely on…
ClusterRCA: An End-to-End Approach for Network Fault Localization and Classification for HPC System
Yongqian Sun, Xijie Pan, Xiao Xiong +6
Network failure diagnosis is challenging yet critical for high-performance computing (HPC) systems. Existing methods cannot be directly applied to HPC scenarios due to data heterog…
TrioXpert: An Automated Incident Management Framework for Microservice System
Yongqian Sun, Yu Luo, Xidao Wen +5
Automated incident management plays a pivotal role in large-scale microservice systems. However, many existing methods rely solely on single-modal data (e.g., metrics, logs, and tr…
An Empirical Study of Production Incidents in Generative AI Cloud Services
Haoran Yan, Yinfang Chen, Minghua Ma +10
The ever-increasing demand for generative artificial intelligence (GenAI) has motivated cloud-based GenAI services such as Azure OpenAI Service and Amazon Bedrock. Like any large-s…
Adapting Large Language Models to Log Analysis with Interpretable Domain Knowledge
Yuhe Ji, Yilun Liu, Feiyu Yao +10
Log analysis represents a critical sub-domain within AI applications that facilitates automatic approaches to fault and error management of large-scaled software systems, saving la…