FSE 2025
Mon 23 - Fri 27 June 2025 Trondheim, Norway
co-located with ISSTA 2025
Tue 24 Jun 2025 16:00 - 16:20 at Pirsenteret 150 - Anomaly Detection Chair(s): Gias Uddin

Microservice-based systems (MSS) may fail with various fault types, due to their complex and dynamic nature. While existing AIOps tools excel at detecting abnormal traces and pinpointing the responsible service(s), human efforts from practitioners are still required for further root cause analysis (RCA) to diagnose specific fault types and analyze failure reasons for detected abnormal traces, particularly when abnormal traces do not stem directly from specific services. This paper presents TraFaultDia, a novel framework aimed at automatically classifying abnormal traces into precise fault categories for different MSS. We approach the automatic categorization of abnormal traces into fault types as a series of multi-class classification tasks, each task represents an attempt to classify detected abnormal traces for a MSS. With the classification results from TraFaultDia, practitioners can quickly know fault types of abnormal traces and understand their nature of failures and potential impacts, thereby reducing the time and effort required for manual analysis. TraFaultDia is trained on several abnormal trace classification tasks with a few labeled instances from a MSS using a meta-learning approach. After training, TraFaultDia can quickly adapt to new, unseen abnormal trace classification tasks with a few labeled instances across MSS. We evaluated TraFaultDia on two representative MSS, TrainTicket and OnlineBoutique, with open datasets. Our results show that, within the MSS it is trained on, TraFaultDia achieves an average accuracy of 93.26% and 85.2% across 50 new, unseen abnormal trace classification tasks for TrainTicket and OnlineBoutique respectively, when provided with 10 labeled instances for each fault category per task in each system. In the cross-system context, when TraFaultDia is applied to a MSS different from the one it is trained on, TraFaultDia gets an average accuracy of 92.19% and 84.77% for the same set of 50 new, unseen abnormal trace classification tasks of the respective system, also with 10 labeled instances provided for each fault category per task in each system.

Tue 24 Jun

Displayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change

16:00 - 17:40
Anomaly DetectionIdeas, Visions and Reflections / Research Papers / Industry Papers at Pirsenteret 150
Chair(s): Gias Uddin York University, Canada
16:00
20m
Talk
Cross-System Categorization of Abnormal Traces in Microservice-Based Systems via Meta-Learning
Research Papers
Yuqing Wang University of Helsinki, Finland, Mika Mäntylä University of Helsinki and University of Oulu, Serge Demeyer University of Antwerp and Flanders Make vzw, Mutlu Beyazıt University of Antwerp and Flanders Make vzw, Joanna Kisaakye University of Antwerp, Belgium, Jesse Nyyssölä University of Helsinki
DOI
16:20
10m
Talk
CLSLog: Collaborating Large and Small Models for Log-based Anomaly Detection
Ideas, Visions and Reflections
Pei Xiao Peking University, Tong Jia Institute for Artificial Intelligence, Peking University, Beijing, China, Chiming Duan Peking University, Minghua He Peking University, Weijie Hong Peking university, Xixuan Yang School of Software and Microelectronics, Peking University, Yihan Wu National Computer Network Emergency Response Technical Team/Coordination Center of China, Ying Li School of Software and Microelectronics, Peking University, Beijing, China, Gang Huang Peking University
16:30
10m
Talk
From Few-Label to Zero-Label: An Approach for Cross-System Log-Based Anomaly Detection with Meta-Learning
Ideas, Visions and Reflections
Xinlong Zhao Peking University, Tong Jia Institute for Artificial Intelligence, Peking University, Beijing, China, Minghua He Peking University, Yihan Wu National Computer Network Emergency Response Technical Team/Coordination Center of China, Ying Li School of Software and Microelectronics, Peking University, Beijing, China, Gang Huang Peking University
16:40
20m
Talk
CAShift: Benchmarking Log-Based Cloud Attack Detection under Normality Shift
Research Papers
Jiongchi Yu Singapore Management University, Xiaofei Xie Singapore Management University, Qiang Hu Tianjin University, Bowen Zhang Singapore Management University, Ziming Zhao Zhejiang University, Yun Lin Shanghai Jiao Tong University, Lei Ma The University of Tokyo & University of Alberta, Ruitao Feng Southern Cross University, Frank Liauw Government Technology Agency Singapore
DOI Pre-print
17:00
20m
Talk
Detecting and Handling WoT Violations by Learning Physical Interactions from Device Logs
Research Papers
Bingkun Sun Fudan University, Shiqi Sun Northwestern Polytechnique University, Jialin Ren Fudan University, Mingming Hu Fudan University, Kun Hu School of Computer Science, Fudan University, Liwei Shen Fudan University, Xin Peng Fudan University
DOI
17:20
20m
Talk
L4: Diagnosing Large-scale LLM Training Failures via Automated Log Analysis
Industry Papers
Zhihan Jiang The Chinese University of Hong Kong, Junjie Huang The Chinese University of Hong Kong, Guangba  Yu The Chinese University of Hong Kong, Zhuangbin Chen Sun Yat-sen University, Yichen LI The Chinese University of Hong Kong, Renyi Zhong The Chinese University of Hong Kong, Cong Feng Huawei Cloud Computing Technology, Yongqiang Yang Huawei Cloud Computing Technology, Zengyin Yang Computing and Networking Innovation Lab, Huawei Cloud Computing Technology Co., Ltd, Michael Lyu Chinese University of Hong Kong

Information for Participants
Tue 24 Jun 2025 16:00 - 17:40 at Pirsenteret 150 - Anomaly Detection Chair(s): Gias Uddin
Info for room Pirsenteret 150:

This room is located outside Clarion Hotel

This room is located in the Pirsenteret (The Pier Center) convention center. It is just outside the hotel, on the back, towards the fjord.

You should be able to go through the emergency exit at Clarion, just on the side of the Cosmos 3 wing, which will be bring you close to Pirsenteret.

The entrance to the center is from here:
https://maps.app.goo.gl/dU3qH6kAimXGBNHe7
Once inside, go all straight and you will find signage to reach the room. The room is known as room 150 inside the center.

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