  Context: Logs are often the primary source of information for system
developers and operations engineers to understand and diagnose the behavior of
a software system in production. In many cases, logs are the only evidence
available for fault investigation. Problem: However, the inappropriate choice
of log severity level can impact the amount of log data generated and,
consequently, quality. This storage overhead can impact the performance of
log-based monitoring systems, as excess log data comes with increased aggregate
noise, making it challenging to utilize what is actually important when trying
to do diagnostics. Goal: This research aims to decrease the overheads of
monitoring systems by processing the severity level of log data from systems in
production. Approach: To achieve this goal, we intend to deepen the knowledge
about the log severity levels and develop an automated approach to log severity
level classification, demonstrating that reducing log severity level "noise"
improves the monitoring of systems in production. Conclusion: We hope that the
set of contributions from this work can improve the monitoring activities of
software systems and contribute to the creation of knowledge that improves
logging practices
