Logs are semi-structured text generated by logging statements in software source code. In
recent decades, software logs have become imperative in the reliability assurance …

Automatic log file analysis enables early detection of relevant incidents such as system
failures. In particular, self-learning anomaly detection techniques capture patterns in log …

Software-intensive systems produce logs for troubleshooting purposes. Recently, many
deep learning models have been proposed to automatically detect system anomalies based …

Detecting anomalous events in online computer systems is crucial to protect the systems
from malicious attacks or malfunctions. System logs, which record detailed information of …

Software systems often record important runtime information in system logs for
troubleshooting purposes. There have been many studies that use log data to construct …

Log parsing transforms log messages into structured formats, serving as the prerequisite
step for various log analysis tasks. Although a variety of log parsing approaches have been …

Log data have facilitated various tasks of software development and maintenance, such as
testing, debugging and diagnosing. Due to the unstructured nature of logs, log parsing is …

With the rapid development of IT operations, it has become increasingly crucial to efficiently
manage and analyze large volumes of data for practical applications. The techniques of …

Log parsing, which extracts log templates and parameters, is a critical prerequisite step for
automated log analysis techniques. Though existing log parsers have achieved promising …

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 …