Papers › On Automatic Parsing of Log Records
On Automatic Parsing of Log Records
Jared Rand, Andriy Miranskyy
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.
Code
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Tasks
Datasets
Introduced by this paper, per the archive.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Machine Translation | V_A (trained on T_H) | M_C | Median Relative Edit Distance | 0.28 | #1 of 1 | Archive leaderboard | report |
| Machine Translation | V_B (trained on T_H) | M_C | Median Relative Edit Distance | 0.25 | #1 of 1 | Archive leaderboard | report |
| Machine Translation | V_C (trained on T_H) | M_C | Median Relative Edit Distance | 0.27 | #1 of 1 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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