2 research outputs found
Log-based software monitoring: a systematic mapping study
Modern software development and operations rely on monitoring to understand
how systems behave in production. The data provided by application logs and
runtime environment are essential to detect and diagnose undesired behavior and
improve system reliability. However, despite the rich ecosystem around
industry-ready log solutions, monitoring complex systems and getting insights
from log data remains a challenge.
Researchers and practitioners have been actively working to address several
challenges related to logs, e.g., how to effectively provide better tooling
support for logging decisions to developers, how to effectively process and
store log data, and how to extract insights from log data. A holistic view of
the research effort on logging practices and automated log analysis is key to
provide directions and disseminate the state-of-the-art for technology
transfer.
In this paper, we study 108 papers (72 research track papers, 24 journals,
and 12 industry track papers) from different communities (e.g., machine
learning, software engineering, and systems) and structure the research field
in light of the life-cycle of log data.
Our analysis shows that (1) logging is challenging not only in open-source
projects but also in industry, (2) machine learning is a promising approach to
enable a contextual analysis of source code for log recommendation but further
investigation is required to assess the usability of those tools in practice,
(3) few studies approached efficient persistence of log data, and (4) there are
open opportunities to analyze application logs and to evaluate state-of-the-art
log analysis techniques in a DevOps context