  Artificial Intelligence for IT Operations (AIOps) leverages AI approaches to
handle the massive amount of data generated during the operations of software
systems. Prior works have proposed various AIOps solutions to support different
tasks in system operations and maintenance, such as anomaly detection. In this
study, we conduct an in-depth analysis of open-source AIOps projects to
understand the characteristics of AIOps in practice. We first carefully
identify a set of AIOps projects from GitHub and analyze their repository
metrics (e.g., the used programming languages). Then, we qualitatively examine
the projects to understand their input data, analysis techniques, and goals.
Finally, we assess the quality of these projects using different quality
metrics, such as the number of bugs. To provide context, we also sample two
sets of baseline projects from GitHub: a random sample of machine learning
projects and a random sample of general-purposed projects. By comparing
different metrics between our identified AIOps projects and these baselines, we
derive meaningful insights. Our results reveal a recent and growing interest in
AIOps solutions. However, the quality metrics indicate that AIOps projects
suffer from more issues than our baseline projects. We also pinpoint the most
common issues in AIOps approaches and discuss potential solutions to address
these challenges. Our findings offer valuable guidance to researchers and
practitioners, enabling them to comprehend the current state of AIOps practices
and shed light on different ways of improving AIOps' weaker aspects. To the
best of our knowledge, this work marks the first attempt to characterize
open-source AIOps projects.
