LogAssist: Assisting Log Analysis Through Log Summarization
Steven Locke, Heng Li, Tse-Hsun Peter Chen, Weiyi Shang, Wei Liu · IEEE Transactions on Software Engineering · 2021
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 calledLogAssistthat assists practitioners with log analysis.LogAssistprovides 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,LogAssistcompresses the log events in workflows by hiding consecutive events and applying n-gram modeling to identify common event sequences. We evaluatedLogAssiston logs generated by one enterprise and two open source systems. We find thatLogAssistcan 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 thatLogAssistcan assist practitioners by reducing the time required for log analysis tasks by an average of 40 percent. The participants also ratedLogAssistan 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 adoptingLogAssistin practice. We believe thatLogAssistand our reported experiences may lay the basis for future analysis and interactive exploration on logs.