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.