基于多样本学习的少样本学习日志解析
Log Parsing with Prompt-based Few-shot Learning
摘要：
大型软件系统产生的故障信息为工程师了解系统状态和诊断系统问题提供了重要信息。日志解析将原始日志消息转换为结构化数据，这是启用自动日志分析的第一步。现有的日志解析器使用统计特征提取公共部分作为日志模板。然而，这些日志解析器经常无法识别正确的模板和参数，因为：1）它们经常忽略日志消息的语义含义，以及2）它们需要针对不同日志数据集的特定于域的知识。为了解决现有方法的局限性，在本文中，我们提出LogPPT使用基于模板的少样本学习来捕获模板的模式。LogPPT利用一种新的提示微调方法来识别关键字和参数的基础上，一些标记的日志数据。此外，自适应随机抽样算法的设计，以选择一个小而多样的训练集。我们在16个公共日志数据集上进行了广泛的实验。实验结果表明，LogPPT是一种有效的日志解析工具。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
