About Me:
I am a doctoral researcher in the Department of Computer Science at the University of Helsinki. My research area is AIOps (Artificial Intelligence for IT Operations), with a focus on making microservice systems easier and more reliable to operate. I am passionate about bridging the gap between theoretical research and practical applications, working to enhance the operational capabilities of microservice systems in industrial scenarios.
My research:
Modern microservice systems (MSS) emit large volumes of heterogeneous telemetry, such as logs, metrics, and traces. Faults propagate across service, deployment, and resource dependencies, so the visible symptoms often appear far from where a fault originates. My doctoral research builds toward a closed loop of automated incident management for MSS, in three stages:
- Data foundation. Progress on anomaly detection and root cause analysis in MSS depends on realistic, high-quality data, yet public benchmarks remain scarce and typically cover only narrow fault types and few monitoring modalities. My first work addresses this gap by constructing a multimodal anomaly dataset for MSS that covers diverse anomaly categories and rich telemetry modalities, providing a foundation for cross-modal detection and fine-grained root cause analysis.
- Fault diagnosis. Building on this foundation, my second work studies how to localize the origin of faults in MSS from multimodal telemetry, and how to keep the resulting diagnoses interpretable by grounding them in the telemetry evidence that supports them.
- Auto-remediation. Detection and diagnosis are only useful if incidents actually get fixed. The final stage of my research develops an LLM-agent-based auto-remediation system for MSS that turns diagnoses and operational knowledge into executable remediation plans and carries them out with minimal human intervention.
Publications:
Ying Song, Ke Ping, Yuqing Wang, Xiaozhou Li. Large Language Models for Fuzz Testing in Microservices: A Systematic Literature Review. The 52nd Euromicro Conference on Software Engineering and Advanced Applications (SEAA 2026), 2026.
Ping, Ke, Hamza Mazhar, Yuqing Wang, Ying Song, and Mika V. Mäntylä. “AnoMod: A Dataset for Anomaly Detection and Root Cause Analysis in Microservice System.” In Proceedings of the 23rd International Conference on Mining Software Repositories, pp. 677-681. 2026.
Wang, Yuqing, Mika V. Mäntylä, Jesse Nyyssölä, Ke Ping, and Liqiang Wang. “Cross-system software log-based anomaly detection using meta-learning.” In 2025 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER), pp. 454-464. IEEE, 2025.
Bakhtin, Alexander, Jesse Nyyssölä, Yuqing Wang, Noman Ahmad, Ke Ping, Matteo Esposito, Mika Mäntylä, and Davide Taibi. “Lo2: Microservice api anomaly dataset of logs and metrics.” In Proceedings of the 21st International Conference on Predictive Models and Data Analytics in Software Engineering, pp. 1-10. 2025.
Contact Details:
ke.ping@helsinki.fi