Journal Article

Discovering boundary values of feature-based machine learning classifiers through exploratory datamorphic testing

Abstract

Testing has been widely recognised as difficult for AI applications. This paper proposes a set of testing strategies for testing machine learning applications in the framework of the datamorphism testing methodology. In these strategies, testing aims at exploring the data space of a classification or clustering application to discover the boundaries between classes that the machine learning application defines. This enables the tester to understand precisely the behaviour and function of the software under test. In the paper, three variants of exploratory strategies are presented with the algorithms implemented in the automated datamorphic testing tool Morphy. The correctness of these algorithms are formally proved. Their capability and cost of discovering borders between classes are evaluated via a set of controlled experiments with manually designed subjects and a set of case studies with real machine learning models.

Attached files

Authors

Zhu Hong
Bayley, Ian

Oxford Brookes departments

School of Engineering, Computing and Mathematics

Dates

Year of publication: 2022
Date of RADAR deposit: 2022-01-26

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License

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Related resources

This RADAR resource is the Accepted Manuscript of Discovering boundary values of feature-based machine learning classifiers through exploratory datamorphic testing
This RADAR resource is the Accepted Manuscript of [arXiv preprint] Discovering boundary values of feature-based machine learning classifiers through exploratory datamorphic testing