Using Metamorphic Relations to Verify and Enhance Artcode Classification

TL;DR

This paper applies metamorphic testing to verify and improve image classifiers for Artcodes, demonstrating that MR-augmented classifiers outperform non-augmented ones through statistical analysis and experimental evaluation.

Contribution

It introduces a novel application of metamorphic testing to Artcode classifiers and proposes MR-augmented classifiers that show improved performance over traditional models.

Findings

MRs effectively verify image classification accuracy.

MR-augmented classifiers outperform non-augmented classifiers.

Statistical analysis confirms performance improvements.

Abstract

Software testing is often hindered where it is impossible or impractical to determine the correctness of the behaviour or output of the software under test (SUT), a situation known as the oracle problem. An example of an area facing the oracle problem is automatic image classification, using machine learning to classify an input image as one of a set of predefined classes. An approach to software testing that alleviates the oracle problem is metamorphic testing (MT). While traditional software testing examines the correctness of individual test cases, MT instead examines the relations amongst multiple executions of test cases and their outputs. These relations are called metamorphic relations (MRs): if an MR is found to be violated, then a fault must exist in the SUT. This paper examines the problem of classifying images containing visually hidden markers called Artcodes, and applies MT to verify and enhance the trained classifiers. This paper further examines two MRs, Separation and Occlusion, and reports on their capability in verifying the image classification using one-way analysis of variance (ANOVA) in conjunction with three other statistical analysis methods: t-test (for unequal variances), Kruskal-Wallis test, and Dunnett's test. In addition to our previously-studied classifier, that used Random Forests, we introduce a new classifier that uses a support vector machine, and present its MR-augmented version. Experimental evaluations across a number of performance metrics show that the augmented classifiers can achieve better performance than non-augmented classifiers. This paper also analyses how the enhanced performance is obtained.