Conference Paper


Datamorphic testing: A method for testing intelligent applications

Abstract

Adequate testing of AI applications is essential to ensure their quality. However, it is often prohibitively difficult to generate realistic test cases or to check software correctness. This paper proposes a new method called datamorphic testing, which consists of three components: a set of seed test cases, a set of datamorphisms for transforming test cases, and a set of metamorphisms for checking test results. With an example of face recognition application, the paper demonstrates how to develop datamorphic test frameworks, and illustrates how to perform testing in various strategies, and validates the approach using an experiment with four real industrial applications of face recognition.

Attached files

Authors

Zhu, Hong
Liu, Dongmei
Bayley, Ian
Harrison, Rachel
Cuzzolin, Fabio

Oxford Brookes departments

Faculty of Technology, Design and Environment\School of Engineering, Computing and Mathematics

Dates

Year of publication: 2019
Date of RADAR deposit: 2019-05-31



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Related resources

This RADAR resource is the Accepted Manuscript of Datamorphic testing: A method for testing intelligent applications

Details

  • Owner: Daniel Croft
  • Collection: Outputs
  • Version: 1 (show all)
  • Status: Live