TraceLens: Early Detection of Software Anomalies Using Critical Path Analysis

Runtime smell detection in software systems, particularly through system call analysis, has garnered significant attention in recent years. Although various machine learning techniques have been employed to enhance detection accuracy and reduce false positives, limited focus has been given to their practical application in early real-time anomaly detection. To address this gap, we propose a deep learning-based approach, called TraceLens, designed for the early detection of performance-related issues in software systems. Unlike traditional methods that rely on system call data, our approach leverages critical path analysis, enabling more efficient and targeted anomaly detection. Experimental results demonstrate that this approach achieves detection performance comparable to methods that use system calls, while significantly improving data collection efficiency. In addition, the critical path dataset highlights software dependencies, both internal and external, providing deeper insight into the dynamic behavior of software systems.