A Semi-Automated Approach for Resolving Data-Driven Architecture Mismatches

In contemporary software development, there is a need for delivering solutions that require the integration of multiple software systems, each one relying on different architec-tural decisions. For instance, e-shop solutions must communi-cate with the ERP solution that the company possesses to handle prices, products, and stock. However, such an integration is not always a trivial issue since interoperability problems might arise. A root cause for such interoperability issues is architecture mismatches: e.g., caused by heterogeneity on how data are stored and are expected to be exchanged in the two systems. In-teroperability problems can cause delays to the development, require extended communication with different teams, and usually adds complexity to the system. In this paper, we propose a semi-automated AI-based approach and a middleware software solution (“a connector”) to aid software engineers in “connecting” applications with heterogeneous data storing schemas. We have validated our approach and tool with a company that connects ERP systems with e-shops, through a qualitative study.