On Automatic Parsing of Log Records
This work addresses the laborious task of manual log parsing for software maintenance, though it is incremental as it applies existing MT methods to a new domain.
The authors tackled the problem of automating log parsing by using machine translation models trained on synthetic Apache logs, achieving a median relative edit distance of ≤28% on real-world logs.
Software log analysis helps to maintain the health of software solutions and ensure compliance and security. Existing software systems consist of heterogeneous components emitting logs in various formats. A typical solution is to unify the logs using manually built parsers, which is laborious. Instead, we explore the possibility of automating the parsing task by employing machine translation (MT). We create a tool that generates synthetic Apache log records which we used to train recurrent-neural-network-based MT models. Models' evaluation on real-world logs shows that the models can learn Apache log format and parse individual log records. The median relative edit distance between an actual real-world log record and the MT prediction is less than or equal to 28%. Thus, we show that log parsing using an MT approach is promising.