Semantic Log Partitioning: Towards Automated Root Cause Analysis

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.