Towards Automated Data Integration in Software Analytics
This work addresses the challenge of data integration for software analytics stakeholders, but it appears incremental as it builds on existing ontology-based methods without demonstrating major breakthroughs.
The paper tackles the problem of integrating heterogeneous data sources for software analytics by proposing an ontology-based approach with static and dynamic methods, aiming to support real-time decision-making and information transparency for software organizations.
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.