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Deep learning (DL) has achieved unprecedented success in a variety of tasks.However, DL systems are notoriously difficult to test and debug due to the lackof explainability of DL models and the huge test input space to cover.Generally speaking, it is relatively easy to collect a massive amount of testdata, but the labeling cost can be quite high. Consequently, it is essential toconduct test selection and label only those selected "high quality"bug-revealing test inputs for test cost reduction.  In this paper, we propose a novel test prioritization technique that bringsorder into the unlabeled test instances according to their bug-revealingcapabilities, namely TestRank. Different from existing solutions, TestRankleverages both intrinsic attributes and contextual attributes of test instanceswhen prioritizing them. To be specific, we first build a similarity graph ontest instances and training samples, and we conduct graph-based semi-supervisedlearning to extract contextual features. Then, for a particular test instance,the contextual features extracted from the graph neural network (GNN) and theintrinsic features obtained with the DL model itself are combined to predictits bug-revealing probability. Finally, TestRank prioritizes unlabeled testinstances in descending order of the above probability value. We evaluate theperformance of TestRank on a variety of image classification datasets.Experimental results show that the debugging efficiency of our methodsignificantly outperforms existing test prioritization techniques.

TestRank: Bringing Order into Unlabeled Test Instances for Deep Learning Tasks