基于机器学习的日志函数自动识别方法

随着软件规模的不断增长,日志在故障检测中发挥着愈加重要的作用。然而,目前软件日志缺乏统一标准,常受开发人员个人习惯影响,为大规模系统中日志的自动化分析带来了挑战。其中,日志函数的识别作为日志分析的前提条件,对分析结果有着直接影响。提出了一种基于机器学习的方法以支持日志自动识别。通过系统分析广泛使用的大规模开源软件,总结出日志函数编写的主要形式,并提取不同形式间的共性特征,进而基于机器学习实现了自动日志识别工具iLog。实验显示,使用iLog识别的日志函数能力平均为使用特定关键字的76倍,十折交叉验证得到iLog的分析结果的F-Score为0.93。

Logging function recognition based on machine learning technique

With software scaling up continuously, logging mechanism has become an indispensable part in failure diagnosis area. A pretty similar symptom may be caused by various software bugs, and the most obvious evidence is always logging messages. Meanwhile, the development of most pieces of large scale software is affected by developers' personal habits rather than being guided by certain conventional specification, so log related analysis suffers in large scale software. The recognition of logging function plays a precondition role in log analysis and affects the results of log analysis directly. We propose a machine learning method to fill the gap that logging function recognition has not been paid attention by most existing log related works. Learning from widely used software, we summary three logging functions, extract five common features to complement automated logging function recognition tool iLog based on machine learning. Evaluations show that the recognition ability of iLog is 76 times of those using key words. Additionally, 10 fold cross validation shows that the F Score average is 0.93.