Towards Automated Data Integration in Software Analytics

TL;DR

This paper proposes an ontology-based approach for integrating heterogeneous software analytics data sources to enable real-time decision-making and customizable analytics in software organizations.

Contribution

It introduces a novel ontology framework and static/dynamic integration approaches for real-time, automated data integration in software analytics.

Findings

Ontology captures semantics of software analytics data.

Static and dynamic integration approaches are proposed.

Framework supports real-time, customizable analytics.

Abstract

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.