# Datamorphic Testing: A Methodology for Testing AI Applications

arXiv: https://arxiv.org/abs/1912.04900 · HTML: https://mlsanity.com/paper/1912.04900/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Dec 2019.

## Built on by (most cited first, 1 of 1)

- [Discovering Boundary Values of Feature-based Machine Learning Classifiers through Exploratory Datamorphic Testing](https://mlsanity.com/paper/2110.00330/) (2021)

## Abstract (arXiv)

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.