  The tools employed in the DevOps Toolchain generates a large quantity of data
that is typically ignored or inspected only in particular occasions, at most.
However, the analysis of such data could enable the extraction of useful
information about the status and evolution of the project. For example, metrics
like the "lines of code added since the last release" or "failures detected in
the staging environment" are good indicators for predicting potential risks in
the incoming release. In order to prevent problems appearing in later stages of
production, an anomaly detection system can operate in the staging environment
to compare the current incoming release with previous ones according to
predefined metrics. The analysis is conducted before going into production to
identify anomalies which should be addressed by human operators that address
false-positive and negatives that can appear. In this paper, we describe a
prototypical implementation of the aforementioned idea in the form of a "proof
of concept". The current study effectively demonstrates the feasibility of the
approach for a set of implemented functionalities.
