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Introduction to Metamorphic Testing on Classifiers

Written by Oreoluwa Alebiosu on September 15, 2014.

A considerable amount of research on metamorphic testing on classifiers is driven by Columbia University. This blog provides an overview of work done so far in the metamorphic testing approach used for classifier algorithms in data mining/machine learning. The majority of the research effort in the domain of machine learning focuses on building more accurate models that can better achieve the goal of automated learning from the real world. However, to date very little work has been done on assuring the correctness of the software applications that perform machine learning.

Metamorphic testing is a testing technique that uses properties of functions such that it is possible to predict expected changes to the output for particular changes to the input, based on so-called "metamorphic relations" between given sets of inputs and their corresponding outputs. Xie et al. notes that although the correct output cannot be known in advance, if the change is not as expected, then a defect must exist.

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