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.

The performance of local binary pattern (LBP) and many LBP-based variants is usually limited by rotation, illumination, scale, viewpoint, and the number of training samples. In view of this, this letter presents a robust image descriptor named crosscomplementary LBP (CCLBP) for texture classification. Based on the continuous rotation invariance and highly discriminative characteristic of principal curvatures, significant local geometrical information, which is complementary to LBP is obtained. Then, the resulting information is quantized and encoded into a binary pattern. To enhance the robustness to scale, viewpoint, and the number of training samples, a multiscale and multiresolution analysis is explored by diversifying two parameters accordantly. Subsequently, a cross-scale joint feature representation is conducted on the generated complementary binary responses, resulting in the proposed CCLBP, which captures a highly discriminative information but with low dimensionality. Experimental results on three standard texture databases demonstrate that the proposed CCLBP achieves competitive performance or outperforms state-of-the-art texture descriptors while enjoying a succinct feature representation. Impressively, under the premise of maintaining complete rotation invariance, the performance of the CCLBP approach against illumination, viewpoint, and scale changes has been improved, especially when the number of training samples is limited.