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Proteins are the workhorses of life and gaining insight on their functions isof paramount importance for applications such as drug design. However, theexperimental validation of functions of proteins is highly-resource consuming.Therefore, recently, automated protein function prediction (AFP) using machinelearning has gained significant interest. Many of these AFP tools are based onsupervised learning models trained using existing gold-standard functionalannotations, which are known to be incomplete. The main challenge associatedwith conducting systematic testing on AFP software is the lack of a testoracle, which determines passing or failing of a test case; unfortunately, dueto the incompleteness of gold-standard data, the exact expected outcomes arenot well defined for the AFP task. Thus, AFP tools face the \emph{oracleproblem}. In this work, we use metamorphic testing (MT) to test ninestate-of-the-art AFP tools by defining a set of metamorphic relations (MRs)that apply input transformations to protein sequences. According to ourresults, we observe that several AFP tools fail all the test cases causingconcerns over the quality of their predictions.

Metamorphic Testing for Quality Assurance of Protein Function Prediction Tools