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Authors: Daniel Braun ; Anupama Sajwan and Florian Matthes

Affiliation: Technical University of Munich, Department of Informatics, Munich, Germany

ISBN: 978-989-758-423-7

ISSN: 2184-4992

Keyword(s): Natural Language Generation, Regression Testing, Finance.

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.

CC BY-NC-ND 4.0

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Paper citation in several formats:
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 ISSN 2184-4992, pages 613-618. DOI: 10.5220/0009563306130618

@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},
issn={2184-4992},
}

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
IS - 2184-4992
AU - Braun, D.
AU - Sajwan, A.
AU - Matthes, F.
PY - 2020
SP - 613
EP - 618
DO - 10.5220/0009563306130618

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