Research Focus
My doctoral research focuses on multimodal anomaly detection in microservice systems, leveraging heterogeneous observability data such as logs, traces, and metrics. I investigate methods for integrating and analyzing these data modalities to effectively detect system anomalies and improve the reliability of complex microservice-based systems.
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. Accepted.
- Ke Ping, Hamza Mazhar, Yuqing Wang, Ying Song, Mika V. Mäntylä. AnoMod: A Dataset for Anomaly Detection and Root Cause Analysis in Microservice System. Proceedings of the 23rd International Conference on Mining Software Repositories (MSR 2026), pp. 677–681, 2026.
- Yuqing Wang, Ying Song, Xiaozhou Li, Nana Reinikainen, Mika V. Mäntylä. A Comparative Study of Semantic Log Representations for Software Log-based Anomaly Detection. arXiv preprint arXiv:2604.08028, 2026.
- Jesse Nyyssölä, Hamza Bin Mazhar, Alexander Bakhtin, Matteo Esposito, Nana Reinikainen, Yuqing Wang, Ying Song, Davide Taibi, Mika Mäntylä. Towards LLM Accelerated Rapid Reviews for Software Tool Discovery – Case for Log Anomaly Detection. The 52nd Euromicro Conference on Software Engineering and Advanced Applications (SEAA 2026), 2026. Accepted.