User-adaptable natural language generation for regression testing within the finance domain

Anupama Sajwan, Florian Matthes, Daniel Braun

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationICEIS 2020 - Proceedings of the 22nd International Conference on Enterprise Information Systems
EditorsJoaquim Filipe, Michal Smialek, Alexander Brodsky, Slimane Hammoudi
PublisherSciTePress
Pages613-618
Number of pages6
ISBN (Electronic)9789897584237
StatePublished - 2020
Event22nd International Conference on Enterprise Information Systems, ICEIS 2020 - Virtual, Online
Duration: 5 May 20207 May 2020

Publication series

NameICEIS 2020 - Proceedings of the 22nd International Conference on Enterprise Information Systems
Volume1

Conference

Conference22nd International Conference on Enterprise Information Systems, ICEIS 2020
CityVirtual, Online
Period5/05/207/05/20

Keywords

  • Finance
  • Natural Language Generation
  • Regression Testing

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