LogPTR: Variable-Aware Log Parsing with Pointer Network
This addresses the need for more accurate and less labor-intensive log parsing in software development, though it appears incremental as it builds on existing log parsing techniques.
The paper tackled the problem of log parsing for automated log analysis by proposing LogPTR, a variable-aware parser that extracts static and dynamic parts and identifies variable categories, which outperformed state-of-the-art methods on benchmark datasets.
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 they 1) ignore categories of variables, and 2) need labor-intensive model tuning. To address these limitations, we propose LogPTR, a variable-aware log parser that can extract the static and dynamic parts in logs, and further identify categories of variables. The key of LogPTR is formulating log parsing as a text summarization problem and using a pointer mechanism to copy words from the log message and label tokens indicating categories of variables. The experimental results on widely-used benchmark datasets show that LogPTR outperforms state-of-the-art log parsers on both general log parsing that extracts log templates and variable-aware log parsing that further identifies categories of variables.