In recent years, the significance of test logs in ensuring system reliability and diagnosing runtime events has grown significantly, particularly with software expanding into various domains, necessitating rigorous verification and validation processes. However, the complexity and cost of testing have prompted a shift towards automation. This paper addresses the challenges of automated software testing through root-cause event detection. The proposed approach initially involves parsing and partitioning logs, followed by representing test events as dense vectors in a continuous space, enabling the capture of semantic similarities and relationships among events based on their sequence positions. Subsequently, test events are clustered in this embedded space, and each log partition is represented as a vector, with its characteristics reflecting the number of events in the log partition present in the clusters. Through two distinct case studies, we demonstrate that the final clustering of log partitions in this new space efficiently identifies root cause events. We evaluate our approach on two applications and anticipate its contribution as a cornerstone for future research and deployment of automated log mining.

Telemetry data (e.g.: CPU and memory usage) is an essential source of information for a software system that projects the system’s health. Anomalies in telemetry data warn system administrators about an imminent failure or deterioration of service quality. However, input events to the system (such as service requests) are the cause of abnormal system behaviour and, thus, anomalous telemetry data. By observing input events, one might predict anomalies even before they appear in telemetry data, thus giving the system administrator even earlier warning before the failure. Finding a correlation between input events and anomalies in telemetry data is challenging in many cases. This paper proposes a machine learning approach to learn the causality correlation between input event sequences and telemetry data. To this aim, a Natural Language Processing(NLP) approach is employed to create a concept space model to distinguish between normal and abnormal test sequences. Based on a vectorized representation of each input sequence, the concept space indicates whether the sequence will cause a system failure. Since the meaning of fault is not established in system status Telemetry-based fault detection, the suggested technique first detects periods of time when a software system status encounters aberrant situations (Bug-Zones). An extensive study on a real-world database acquired by a telecommunication operator and an open-source microservice software demonstrates that our approach achieves 71% and 90% accuracy as a Bug-Zones predictor.