activity
20232026
most citedFaultProfIT: Hierarchical Fault Profiling of Incident Tickets in Large-scale Cloud Systems

11 citations · 31 across the 21 of their papers we have counts for

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Showing 2025Show all

10 papers · 1 filter

cs.SE2025

ErrorPrism: Reconstructing Error Propagation Paths in Cloud Service Systems

Junsong Pu, Yichen Li, Zhuangbin Chen +6

Reliability management in cloud service systems is challenging due to the cascading effect of failures. Error wrapping, a practice prevalent in modern microservice development, enr…

cs.SE2025

LogPilot: Intent-aware and Scalable Alert Diagnosis for Large-scale Online Service Systems

Zhihan Jiang, Jinyang Liu, Yichen Li +7

Effective alert diagnosis is essential for ensuring the reliability of large-scale online service systems. However, on-call engineers are often burdened with manually inspecting ma…

cs.SE2025

Trace Sampling 2.0: Code Knowledge Enhanced Span-level Sampling for Distributed Tracing

Yulun Wu, Guangba Yu, Zhihan Jiang +2

Distributed tracing is an essential diagnostic tool in microservice systems, but the sheer volume of traces places a significant burden on backend storage. A common approach to mit…

cs.SE2025

UniSage: A Unified and Post-Analysis-Aware Sampling for Microservices

Zhouruixing Zhu, Zhihan Jiang, Tianyi Yang +1

Traces and logs serve as the backbone of observability in microservice architectures, yet their sheer volume imposes prohibitive storage and computational burdens. To reduce overhe…

cs.SE2025

CCISolver: End-to-End Detection and Repair of Method-Level Code-Comment Inconsistency

Renyi Zhong, Yintong Huo, Wenwei Gu +6

Comments within code serve as a crucial foundation for software documentation, facilitating developers to communicate and understand the code effectively. However, code-comment inc…

cs.SE2025

Towards Mitigating API Hallucination in Code Generated by LLMs with Hierarchical Dependency Aware

Yujia Chen, Mingyu Chen, Cuiyun Gao +3

Application Programming Interfaces (APIs) are crucial in modern software development. Large Language Models (LLMs) assist in automated code generation but often struggle with API h…

cs.SE2025

LLMPrism: Black-box Performance Diagnosis for Production LLM Training Platforms

Zhihan Jiang, Rui Ren, Guangba Yu +8

Large Language Models (LLMs) have brought about revolutionary changes in diverse fields, rendering LLM training of utmost importance for modern enterprises. To meet this demand, mu…

cs.DC2025

Hierarchical Prediction-based Management for LMaaS Systems

Zhihan Jiang, Yujie Huang, Guangba Yu +3

Large Language Models (LLMs) have revolutionized numerous domains, driving the rise of Language-Model-as-a-Service (LMaaS) platforms that process millions of queries daily. These p…

cs.SE2025

COCA: Generative Root Cause Analysis for Distributed Systems with Code Knowledge

Yichen Li, Yulun Wu, Jinyang Liu +4

Runtime failures are commonplace in modern distributed systems. When such issues arise, users often turn to platforms such as Github or JIRA to report them and request assistance.…

cs.SE2025★ 1 cited

L4: Diagnosing Large-scale LLM Training Failures via Automated Log Analysis

Zhihan Jiang, Junjie Huang, Zhuangbin Chen +6

As Large Language Models (LLMs) show their capabilities across various applications, training customized LLMs has become essential for modern enterprises. However, due to the compl…