LogAssist: Assisting Log Analysis Through Log Summarization

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.