LogAssist: Assisting Log Analysis Through Log Summarization

作     者：Locke, Steven Li, Heng Chen, Tse-Hsun (Peter) Shang, Weiyi Liu, Wei 

作者机构：Concordia Univ Software PErformance Anal & Reliabil SPEAR Lab Montreal PQ H3G 1M8 Canada Concordia Univ Dept Comp Sci & Software Engn Montreal PQ H3G 1M8 Canada Polytech Montreal Dept Comp Engn & Software Engn Montreal PQ H3T 1J4 Canada 

出 版 物：《IEEE TRANSACTIONS ON SOFTWARE ENGINEERING》 (IEEE Trans Software Eng)

年 卷 期：2022年第48卷第9期

页      面：3227-3241页

核心收录：

主　　题：Task analysis Runtime Tools Testing Faces Anomaly detection Software systems Log analysis log compression n-gram modeling log abstraction workflow characterization log reduction 

摘      要：Logs contain valuable information about the runtime behaviors of software systems. Thus, practitioners rely on logs for various tasks such as debugging, system comprehension, and anomaly detection. However, logs are difficult to analyze due to their unstructured nature and large size. In this paper, we propose a novel approach called LogAssist that assists practitioners with log analysis. LogAssist provides an organized and concise view of logs by first grouping logs into event sequences (i.e., workflows), which better illustrate the system runtime execution paths. Then, LogAssist compresses the log events in workflows by hiding consecutive events and applying n-gram modeling to identify common event sequences. We evaluated LogAssist on logs generated by one enterprise and two open source systems. We find that LogAssist can reduce the number of log events that practitioners need to investigate by up to 99 percent. Through a user study with 19 participants, we find that LogAssist can assist practitioners by reducing the time required for log analysis tasks by an average of 40 percent. The participants also rated LogAssist an average of 4.53 out of 5 for improving their experiences of performing log analysis. Finally, we document our experiences and lessons learned from developing and adopting LogAssist in practice. We believe that LogAssist and our reported experiences may lay the basis for future analysis and interactive exploration on logs.