Accepted by Top Conferences! Multiple Alibaba Cloud Achievements Improve O&M Intelligence Accuracy and Efficiency

The Alibaba Cloud Observability team has successfully published three research achievements jointly with leading universities, including Tsinghua University, Fudan University, and Tongji University. These papers were consecutively accepted by top international academic conferences, specifically the International Conference on Learning Representations (ICLR) 2026, Transactions on Software Engineering (TSE) 2026, and the International Symposium on Software Testing and Analysis (ISSTA) 2025. The work systematically overcomes core technical challenges in metric data augmentation, large-scale semantic parsing, and cross-system exception detection. Traditional operation intelligence tools often struggle with significant engineering implementation hurdles. Key challenges include the semantics gap, where tools fail to understand essential differences in O&M context; the generalization bottleneck, where models fail to adapt to dynamically evolving production environments; and industrial availability constraints requiring high throughput and low latency. Existing methods frequently perform well in stable lab environments but struggle with data distribution drift and annotation costs in real-world systems. To address these issues, the team introduced AutoDA-Timeseries, which enables AI to predict faults with less data using automated data augmentation for time series. Additionally, SemanticLog provides an efficient open-source solution for semantic log parsing, achieving a peak throughput of 1.28 million logs per second while maintaining high accuracy. LogBase establishes the first large-scale semantic log parsing benchmark, expanding data sources significantly compared to previous standards to enable unified evaluation. These innovative technologies have been integrated into product systems such as Cloud Monitor, Simple Log Service, and Application Real-Time Monitoring Service. This integration supports accurate intelligent alerting, in-depth log understanding, and low-threshold intelligent O&M. The team continues to drive technological breakthroughs through academic innovation to promote the large-scale implementation of AIOps for enterprise digital transformation.