User-adaptable Natural Language Generation for Regression Testing within the Finance Domain

Daniel Braun, Anupama Sajwan, Florian Matthes

Abstract

Reporting duties and regression testing within the financial industry produce huge amounts of data which has to be sighted and analyzed by experts. This time-consuming and expensive process does not fit to modern, agile software developing practices with fast update cycles. In this paper, we present a user-adaptable natural language generation system that supports financial experts from the insurance industry in analysing the results from regression tests for Solvency II risk calculations and evaluate it with a group of experts.

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Paper Citation


in Harvard Style

Braun D., Sajwan A. and Matthes F. (2020). User-adaptable Natural Language Generation for Regression Testing within the Finance Domain.In Proceedings of the 22nd International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-758-423-7, pages 613-618. DOI: 10.5220/0009563306130618


in Bibtex Style

@conference{iceis20,
author={Daniel Braun and Anupama Sajwan and Florian Matthes},
title={User-adaptable Natural Language Generation for Regression Testing within the Finance Domain},
booktitle={Proceedings of the 22nd International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2020},
pages={613-618},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009563306130618},
isbn={978-989-758-423-7},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 22nd International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - User-adaptable Natural Language Generation for Regression Testing within the Finance Domain
SN - 978-989-758-423-7
AU - Braun D.
AU - Sajwan A.
AU - Matthes F.
PY - 2020
SP - 613
EP - 618
DO - 10.5220/0009563306130618