一句话：论文揭示日志动态变量含任务相关信息，提出深度学习日志抽象方法VALB，实现变量感知的模板提取。
现有日志抽象技术通常将所有动态变量统一抽象掉，只输出静态模板，但被抽象的动态变量可能包含对下游日志分析有价值的信息。本文通过人工调查和从业者问卷，研究动态变量特征及其在不同任务中的重要性，发现不同类别动态变量记录的信息各异，区分它们有助于日志分析。作者提出深度学习日志抽象方法 VALB，能识别不同类别的动态变量，并在生成模板时保留指定类别动态变量的取值，即变量感知的日志抽象。在广泛使用的日志抽象基准上，VALB 在通用日志抽象任务上优于现有最先进方法，并取得较高的变量感知抽象准确率。研究强调了利用动态变量中重要信息改进日志分析的潜力。
Due to the sheer size of software logs, developers rely on automated techniques for log analysis. One of the first and most important steps of automated log analysis is log abstraction, which parses the raw logs into a structured format. Prior log abstraction techniques aim to identify and abstract all the dynamic variables in logs and output a static log template for automated log analysis. However, these abstracted dynamic variables may also contain important information that is useful to different tasks in log analysis. In this paper, we investigate the characteristics of dynamic variables and their importance in practice, and explore the potential of a variable-aware log abstraction technique. Through manual investigations and surveys with practitioners, we find that different categories of dynamic variables record various information that can be important depending on the given tasks, the distinction of dynamic variables in log abstraction can further assist in log analysis. We then propose a deep learning based log abstraction approach, named VALB, which can identify different categories of dynamic variables and preserve the value of specified categories of dynamic variables along with the log templates (i.e., variable-aware log abstraction). Through the evaluation on a widely used log abstraction benchmark, we find that VALB outperforms other state-of-the-art log abstraction techniques on general log abstraction (i.e., when abstracting all the dynamic variables) and also achieves a high variable-aware log abstraction accuracy that further identifies the category of the dynamic variables. Our study highlights the potential of leveraging the important information recorded in the dynamic variables to further improve the process of log analysis.
DOI 原文 · arXiv · PDF（开放获取） · DBLP
@inproceedings{LiLCSHLZ23,
title = {Did We Miss Something Important? Studying and Exploring Variable-Aware Log Abstraction},
author = {Zhenhao Li and Chuan Luo and Tse-Hsun Chen and Weiyi Shang and Shilin He and Qingwei Lin and Dongmei Zhang},
booktitle = {ICSE},
year = {2023},
doi = {10.1109/icse48619.2023.00078}
}