Modern software systems must handle unpredictable user loads while maintaining performance and reliability. This paper proposes an AI-based load testing framework using asynchronous load generation, machine learning models, anomaly detection, and digital twin simulation. The system predicts latency, detects anomalies, and estimates failure thresholds, enabling proactive performance optimization for high-traffic applications.
Kansal et al. (Thu,) studied this question.