This program is tentative and subject to change.
Experience Paper
As cloud service systems grow in scale and complexity, incidents that indicate unplanned interruptions and outages become unavoidable. Rapid and accurate triage of these incidents to the appropriate responsible teams is crucial to maintain service reliability and prevent significant financial losses. However, existing incident triage methods relying on manual operations and predefined rules often struggle with efficiency and accuracy due to the heterogeneity of incident data and the dynamic nature of domain knowledge across multiple teams.
To solve these issues, we propose Triangle, an end-to-end incident triage system based on a Multi-Agent framework. Triangle leverages a semantic distillation mechanism to tackle the issue of semantic heterogeneity in incident data, enhancing the accuracy of incident triage. Additionally, we introduce multi-role agents and a negotiation mechanism to emulate human engineers’ workflows, effectively handling decentralized and dynamic domain knowledge from multiple teams. Furthermore, our system incorporates an automated troubleshooting information collection and mitigation mechanism, reducing the reliance on human labor and enabling fully automated end-to-end incident triage. Extensive experiments conducted on a real-world cloud production environment demonstrate that Triangle significantly improved incident triage accuracy (up to 97%) and reduced Time to Engage (TTE) by as much as 91%, demonstrating substantial operational impact across diverse cloud services.
This program is tentative and subject to change.
Tue 18 NovDisplayed time zone: Seoul change
11:00 - 12:30 | |||
11:00 10mTalk | Learning Project-wise Subsequent Code Edits via Interleaving Neural-based Induction and Tool-based Deduction Research Papers Chenyan Liu Shanghai Jiao Tong University; National University of Singapore, Yun Lin Shanghai Jiao Tong University, Yuhuan Huang Shanghai Jiao Tong University, Jiaxin Chang Shanghai Jiao Tong University, Binhang Qi National University of Singapore, Bo Jiang Bytedance Network Technology, Zhiyong Huang National University of Singapore, Jin Song Dong National University of Singapore | ||
11:10 10mTalk | Coding-Fuse: Efficient Fusion of Code Pre‑Trained Models for Classification Tasks Research Papers Yu Zhao , Lina Gong Nanjing University of Aeronautics and Astronautic, Zhiqiu Huang Nanjing University of Aeronautics and Astronautics, Yuchen Jin Nanjing University of Aeronautics and Astronautics, Mingqiang Wei Nanjing University of Aeronautics and Astronautics | ||
11:20 10mTalk | SE-Jury: An LLM-as-Ensemble-Judge Metric for Narrowing the Gap with Human Evaluation in SE Research Papers Xin Zhou Singapore Management University, Singapore, Kisub Kim DGIST, Ting Zhang Monash University, Martin Weyssow Singapore Management University, Luis F. Gomes Carnegie Mellon University, Guang Yang , Kui Liu Huawei, Xin Xia Zhejiang University, David Lo Singapore Management University | ||
11:30 10mTalk | iKnow: an Intent-Guided Chatbot for Cloud Operations with Retrieval-Augmented Generation Research Papers Junjie Huang The Chinese University of Hong Kong, Yuedong Zhong Sun Yat-sen University, Guangba Yu The Chinese University of Hong Kong, Zhihan Jiang The Chinese University of Hong Kong, Minzhi Yan HCC Lab, Huawei Cloud Computing Technology Co., Ltd, Wenfei Luan HCC Lab, Huawei Cloud Computing Technology Co., Ltd, Tianyu Yang HCC Lab, Huawei Cloud Computing Technology Co., Ltd, Rui Ren Computing and Networking Innovation Lab, Huawei Cloud Computing Technology Co., Ltd, Michael Lyu The Chinese University of Hong Kong | ||
11:40 10mTalk | Aligning LLMs to Fully Utilize the Cross-file Context in Repository-level Code Completion Research Papers Jia Li Tsinghua University, Hao Zhu Peking University, Huanyu Liu , Xianjie Shi Peking University, He Zong aiXcoder, Yihong Dong Peking University, Kechi Zhang Peking University, China, Siyuan Jiang , Zhi Jin Peking University, Ge Li Peking University | ||
11:50 10mTalk | From Sparse to Structured: A Diffusion-Enhanced and Feature-Aligned Framework for Coincidental Correctness Detection Research Papers Huan Xie Chongqing University, Chunyan Liu Chongqing University, Yan Lei Chongqing University, Zhenyu Wu School of Big Data & Software Engineering, Chongqing University, Jinping Wang Chonqing University | ||
12:00 10mTalk | Watson: A Cognitive Observability Framework for the Reasoning of LLM-Powered Agents Research Papers Benjamin Rombaut Centre for Software Excellence, Huawei Canada, Sogol Masoumzadeh Mcgill University, Kirill Vasilevski Huawei Canada, Dayi Lin Centre for Software Excellence, Huawei Canada, Ahmed E. Hassan Queen’s University | ||
12:10 10mTalk | Understanding Software Engineering Agents: A Study of Thought-Action-Result Trajectories Research Papers Islem BOUZENIA University of Stuttgart, Michael Pradel CISPA Helmholtz Center for Information Security | ||
12:20 10mTalk | Triangle: Empowering Incident Triage with Multi-Agent Research Papers Zhaoyang Yu Tsinghua University, Aoyang Fang Chinese University of Hong Kong, Shenzhen, Minghua Ma Microsoft, Jaskaran Singh Walia Microsoft, Chaoyun Zhang Microsoft, Shu Chi Tsinghua University, Ze Li Microsoft Azure, Murali Chintalapati Microsoft Azure, Xuchao Zhang Microsoft, Rujia Wang Microsoft, Chetan Bansal Microsoft Research, Saravan Rajmohan Microsoft, Qingwei Lin Microsoft, Shenglin Zhang Nankai University, Dan Pei Tsinghua University, Pinjia He Chinese University of Hong Kong, Shenzhen | ||