AUTOMATED TESTING METHODOLOGIES FOR DISTRIBUTED WEB SYSTEMS: A MACHINE LEARNING- BASED APPROACH TO ANOMALY DETECTION

Abstract

In the era of modern technologies, distributed web systems play an important role in global IT infrastructures, providing scalability and resilience. However, verification, validation, and testing of these increasingly complex systems remain a major challenge for both specialists working in the field and researchers who want to explore this field. Due to their heterogeneous nature and dynamic interdependencies between existing system components, this important stage of system testing and validation has high complexity. In this context, the paper explores automated testing based on machine learning techniques for anomaly detection in distributed web systems. The methodology used uses advanced machine learning algorithms such as Convolutional Neural Networks, Recurrent Neural Networks and Long-Short Term Memory, K-means Clustering, and Random Forest to analyze data flows and interactions between microservices in real time. It aims to develop a robust framework that includes modules for data collection and preprocessing, model training and validation, as well as an automatic alert system for detected anomalies. Experimental results demonstrate that the proposed approach outperforms traditional testing methods by increasing accuracy and reducing fault identification time.