  Software organizations want to be able to base their decisions on the latest
set of available data and the real-time analytics derived from them. In order
to support "real-time enterprise" for software organizations and provide
information transparency for diverse stakeholders, we integrate heterogeneous
data sources about software analytics, such as static code analysis, testing
results, issue tracking systems, network monitoring systems, etc. To deal with
the heterogeneity of the underlying data sources, we follow an ontology-based
data integration approach in this paper and define an ontology that captures
the semantics of relevant data for software analytics. Furthermore, we focus on
the integration of such data sources by proposing two approaches: a static and
a dynamic one. We first discuss the current static approach with a predefined
set of analytic views representing software quality factors and further
envision how this process could be automated in order to dynamically build
custom user analysis using a semi-automatic platform for managing the lifecycle
of analytics infrastructures.
