Datamorphic Testing: A Methodology for Testing AI Applications
This addresses the need for effective testing methods in AI applications, particularly for face recognition, but appears incremental as it builds on existing testing concepts.
The paper tackles the challenge of testing AI applications by proposing datamorphic testing, a new method illustrated with face recognition examples, and validates it through experiments on four industrial systems.
With the rapid growth of the applications of machine learning (ML) and other artificial intelligence (AI) techniques, adequate testing has become a necessity to ensure their quality. This paper identifies the characteristics of AI applications that distinguish them from traditional software, and analyses the main difficulties in applying existing testing methods. Based on this analysis, we propose a new method called datamorphic testing and illustrate the method with an example of testing face recognition applications. We also report an experiment with four real industrial application systems of face recognition to validate the proposed approach.