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Computer Science > Software Engineering

arXiv:2302.07435 (cs)
[Submitted on 15 Feb 2023]

Title:Log Parsing with Prompt-based Few-shot Learning

Authors:Van-Hoang Le, Hongyu Zhang
View a PDF of the paper titled Log Parsing with Prompt-based Few-shot Learning, by Van-Hoang Le and Hongyu Zhang
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Abstract:Logs generated by large-scale software systems provide crucial information for engineers to understand the system status and diagnose problems of the systems. Log parsing, which converts raw log messages into structured data, is the first step to enabling automated log analytics. Existing log parsers extract the common part as log templates using statistical features. However, these log parsers often fail to identify the correct templates and parameters because: 1) they often overlook the semantic meaning of log messages, and 2) they require domain-specific knowledge for different log datasets. To address the limitations of existing methods, in this paper, we propose LogPPT to capture the patterns of templates using prompt-based few-shot learning. LogPPT utilises a novel prompt tuning method to recognise keywords and parameters based on a few labelled log data. In addition, an adaptive random sampling algorithm is designed to select a small yet diverse training set. We have conducted extensive experiments on 16 public log datasets. The experimental results show that LogPPT is effective and efficient for log parsing.
Comments: Accepted by The 45th International Conference on Software Engineering (ICSE 2023)
Subjects: Software Engineering (cs.SE)
Cite as: arXiv:2302.07435 [cs.SE]
  (or arXiv:2302.07435v1 [cs.SE] for this version)
  https://doi.org/10.48550/arXiv.2302.07435
arXiv-issued DOI via DataCite

Submission history

From: Van-Hoang Le [view email]
[v1] Wed, 15 Feb 2023 02:57:05 UTC (801 KB)
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