Although deep learning (DL) software has been pervasive in various applications, the brittleness of deep neural networks (DNN) hinders their deployment in many tasks especially high-stake ones. To mitigate the risk accompanied with DL software fault, a variety of DNN testing techniques have been proposed such as test case selection. Among those test case selection or prioritization methods, the uncertainty-based ones such as DeepGini have demonstrated their effectiveness in finding DNN’s faults. Recently, TestRank, a learning based test ranking method has shown their out-performance over simple uncertainty-based test selection methods. However, this is achieved with a more complicated design which needs to train a graph convolutional network and a multi-layer Perceptron. In this paper, we propose a novel and lightweight DNN test selection method to enhance the effectiveness of existing simple ones. Besides the DNN model’s uncertainty on test case itself, we take into account model’s uncertainty on its neighbors. This could diversify the selected test cases and improve the effectiveness of existing uncertainty-based test selection methods. Extensive experiments on 5 datasets demonstrate the effectiveness of our approach.

A new anti‐occlusion particle filter object‐tracking method based on feature fusion is proposed in this study. Colour and local binary pattern features are extracted and additively fused with a deterministic coefficient, which is calculated based on the difference between the object features and the background. An integral cumulative histogram is proposed to reduce the computational cost of feature extraction. A new occlusion determination method is proposed, and corresponding tracking strategies are also put forward for various occlusion conditions; in the case of partial occlusion, block tracking is carried out, and in the case of serious occlusion, the least‐square method is used to predict the object position. Context Aware Vision using Image‐based Active Recognition (CAVIAR) and Video Image Retrieval and Analysis Tool (VIRAT) video libraries are used to validate the method. The experimental results show that the proposed method can describe an object effectively and improve tracking stability and robustness under the occlusion conditions.