PriCod: Prioritizing Test Inputs for Compressed Deep Neural Networks

The widespread adoption of Deep Neural Networks (DNNs) has brought remarkable advances in machine learning. However, the computational and memory demands of complex DNNs hinder their deployment in resource-constrained environments. To address this challenge, compressed DNN models have emerged, offering a compromise between efficiency and accuracy. Nonetheless, assessing the performance of these compressed models can demand extensive testing, typically requiring high manual labeling costs, rendering the process resource-intensive and time-consuming. To mitigate these challenges, test input prioritization has emerged as a promising technique aimed at reducing labeling costs by prioritizing inputs that are more likely to be misclassified. This enables the early identification of bug-revealing tests with reduced time and manual labeling effort. In this article, we propose PriCod, a novel test prioritization approach designed for compressed DNNs. PriCod leverages the behavior disparities caused by model compression, along with the embeddings of test inputs, to effectively prioritize potentially misclassified tests. It operates on the premises that significant behavior disparities between the models indicate potential misclassifications and that inputs near decision boundaries are more likely to be misclassified. To this end, PriCod generates two types of features for each test input (i.e., deviation features and embedding features) to capture the prediction deviation caused by model compression and the proximity to decision boundaries, respectively. By combining these features, PriCod predicts the probability of misclassification for each test, ranking tests accordingly. We conduct an extensive study to evaluate the effectiveness of PriCod, comparing it with multiple test prioritization approaches. The experimental results demonstrate the effectiveness of PriCod, with average improvements of 7.43%–55.89% on natural test inputs, 7.92%–52.91% on noisy test inputs, and 7.03%–51.59% on adversarial test inputs, compared with existing test prioritization approaches.