Research Papers

Artificial intelligence is transforming software testing by scaling up test data generation and analysis, creating new possibilities, but also introducing new challenges. One of the common problems with large-scale test data is the lack of traceability between test scenarios and system requirements. The paper addresses this challenge by proposing a traceability solution tailored to an industrial setting employing a data-driven approach. Building on an existing model-based testing framework, the design extends its annotation capabilities through a multilayer taxonomy. The suggested architecture leverages AI techniques for bidirectional mapping: linking requirements to test scripts for coverage analysis and tracing test scripts back to requirements to understand the tested functionality.

In the dynamic landscape of the banking sector, marked by increased operational complexity and regulatory scrutiny, the pursuit of innovation demands a strategic orientation. While offering advantages in cost, quality and speed, this strategic approach to innovation should mandate a cautious consideration of the risks inherent in the integration of emerging technologies. With the continuous advancement of artificial intelligence (AI), particularly generative AI (GenAI), as a method for emerging banking technologies, its wider adoption in the financial industry demands careful consideration of associated risk implications. This paper advocates for a software testing approach that delves into the complexities of banking technology platforms by leveraging generative algorithms to attain extensive test coverage and simultaneously employs rule-based analytics to refine the generated datasets, optimising coverage for faster execution and efficient resource utilisation. Such an approach is in line with a risk-averse innovation strategy, as it balances out the smart creativity.

This TMPA conference paper outlines an experiment in building an obfuscated version of a proprietary financial transactions dataset.

The TMPA conference paper focuses on tools and methods of reconciliation testing.

This TMPA conference paper looks at the industrial experience of using an automated model-based approach for testing trading systems.

The TMPA conference paper describes the authors' experience of implementing machine learning techniques to predict deviations in service workflows duration, long before the post-trade system reports them as not completed on time.