Data Stream Processing in Reconciliation Testing: Industrial Experience

The paper focuses on a research area encompassing tools and methods of reconciliation testing, an approach to software testing that relies on the data reconciliation concept. The importance of such a test approach is steadily increasing across different knowledge domains, triggered by growing data volumes and overall complexity of present-day software systems. The paper describes a software implementation created as part of the authors’ industrial experience in data stream processing for the task of reconciliation testing of complex financial technology systems. The described solution is a Python-based component of an open-source test automation framework built as a Kubernetes-based microservices platform. The paper outlines the advantages and disadvantages of the approach as well as compares it to existing state-of-the-art solutions for data stream analysis and reconciliation.