Towards Logging Noisiness Theory: quality aspects to characterize unwanted log entries

TL;DR

This paper introduces the initial concept of Logging Noisiness, a theory aimed at characterizing and understanding unwanted log entries to improve log quality for better system monitoring.

Contribution

It proposes a foundational framework for defining and analyzing noise in logs, addressing various quality issues affecting log usefulness.

Findings

Identifies key factors affecting log noisiness

Proposes initial steps towards a formal theory of logging noise

Highlights the impact of noise on monitoring effectiveness

Abstract

Context: Logging tasks track the system's functioning by keeping records of evidence that have been analyzed by monitoring and observability activities. For these activities to be effective, it is necessary to consider the quality of the consumed information. Problem: However, the presence of noise - unwanted information - compromises the log files' quality. The noisiness of a log file can be affected among other things by: (i) the wrong severity log choices, (ii) the production of duplicate entries, (iii) the incompleteness of the information, (iv) the inappropriate format of the entries, (v) the amount of information generated. Objective: This work aims to broadly define the concept of noise in the context of logging, proposing the initial steps of Logging Noisiness, a theory on quality aspects to characterize unwanted log entries.