Towards a Classification of Log Parsing Errors

Log parsing is a technique that is used to extract structures from unstructured log data. It is a key enabler for many log analytic tasks including debugging, fault diagnosis, anomaly detection, and AIOps. In recent years, we have seen an increase in the number of log parsing techniques and tools. The accuracy of these tools varies significantly. To improve log parsing, we need to understand the type of errors they make, which is the purpose of this early research track. We achieve this by examining parsing errors of four leading log parsing tools when applied to the parsing of four log datasets, generated from various systems. Based on this analysis, we suggest a preliminary classification of log parsing errors that can be useful not only in improving the accuracy of log parsing tools, but also defining better logging practices. This folder contains the result of the classification for the 4 datasets (Linux, OpenStack, Apache,HealthApp) used in this study with the 4 log parsing tools (Drain, Spell, AEL, ULP)