Performance Testing

Performance testing aims to identify efficiency bottlenecks in software systems, a crucial step for ensuring optimal performance and resource utilization. Current research emphasizes improving the efficiency and accuracy of performance testing, particularly focusing on techniques like AI-driven dynamic warm-up phase estimation for Java applications and fault injection methods for evaluating the resilience of machine learning models to data variations. These advancements leverage machine learning algorithms, including time series classification and gradient boosting, to optimize testing processes and provide more reliable performance metrics, ultimately leading to more robust and efficient software and AI systems.