Due to the sheer size of software logs, developers rely on automated log analysis. Log parsing, which parses semi-structured logs into a structured format, is a prerequisite of automated log analysis. However, existing log parsers are unsatisfactory when applied in practice because: 1) they ignore categories of variables, and 2) have poor generalization ability. To address the limitations of existing approaches, we propose LogPTR, the first end-to-end variable-aware log parser that can extract the static and dynamic parts in logs, and further identify the categories of variables. The key of LogPTR is using pointer network to copy words from the log message. We have performed extensive experiments on 16 public log datasets and the results show that LogPTR outperforms state-of-the-art log parsers both on general log parsing that extracts the log template and variable-aware log parsing that further identifies the category of variables.
