Software testing is an important problem to design a large software system and it is difficult to be solved due to its computational complexity. We try to use program nets to approach this problem. As the first step towards solving software testing problem, this paper provides a technique to generate subnets of a program net and applies this technique to software testing. Firstly, definitions and properties of program nets are introduced based on our previous works, and the explanation of software testing problem is given. Secondly, polynomial algorithms are proposed to generate subnets that can cover all the given program net. Finally, a case study is presented to show how to find subnets covering a given program net by using the proposed algorithms, as well as to show the input test data of the program net for software testing.

Automatic detection of prohibited items is vital in helping security staff be more efficient while improving the public safety index. However, prohibited item detection within X-ray security inspection images is limited by various factors, including the imbalance distribution of categories, diversity of prohibited item scales, and overlap between items. In this paper, we propose to leverage the Poisson blending algorithm with the Canny edge operator to alleviate the imbalance distribution of categories maximally in the X-ray images dataset. Based on this, we improve the cascade network to deal with the other two difficulties. To address the prohibited scale diversity problem, we propose the Re-BiFPN feature fusion method, which includes a coordinate attention atrous spatial pyramid pooling (CA-ASPP) module and a recursive connection. The CA-ASPP module can implicitly extract direction-aware and position-aware information from the feature map. The recursive connection feeds the CA-ASPP module processed multi-scale feature map to the bottom-up backbone layer for further multi-scale feature extraction. In addition, a Rep-CIoU loss function is designed to address the overlapping problem in X-ray images. Extensive experimental results demonstrate that our method can successfully identify ten types of prohibited items, such as Knives, Scissors, Pressure, etc. and achieves 83.4% of mAP, which is 3.8% superior to the original cascade network. Moreover, our method outperforms other mainstream methods by a significant margin.