Timely detection of performance regression issues is critical to ensuring the stability and user experience of software systems. Traditional methods often rely on high-quality annotated data or data distribution assumptions, which cannot effectively adapt to performance changes in dynamic workload environments. To solve this problem, we propose DynamicRegress, a performance regression detection method based on Siamese network and semi-supervised learning. DynamicRegress integrates multi-dimensional key performance indicators (KPIs) with workload context to accurately characterize system states and detect performance regressions in real time. By employing a dual weight-shared LSTM network, DynamicRegress reduces training complexity while retaining strong feature extraction capabilities. Data augmentation and a weighted loss function are incorporated to enhance the learning of minority regression cases, mitigating the class imbalance issue. Additionally, a semi-supervised learning strategy generates high-quality pseudo-labels to expand the training dataset, effectively addressing the challenge of limited labeled data. Experiments on production data from a top-tier global cloud service provider demonstrate that DynamicRegress achieves a superior F1 Score of 0.958 (outperforming the best baseline method by 0.282) while maintaining a low detection latency of 0.006 seconds per KPI pair. DynamicRegress provides a robust adaptive solution for performance regression detection in dynamic and complex software systems, and we have made the code publicly available to facilitate further research.

@inproceedings{paperbot9278,
  title = {Adaptive Performance Regression Detection Using A Semi-Supervised Siamese Network},
  author = {Yongqian Sun and Mengyao Li and Xiao Xiong and Lei Tao and Yimin Zuo and Wenwei Gu and Shenglin Zhang and Junhua Kuang and Yu Luo and Huandong Zhuang and Bowen Deng and Dan Pei},
  booktitle = {ASE},
  year = {2025},
  doi = {10.1109/ase63991.2025.00289}
}