A Prioritization Method for Combinatorial Test Cases Based on Convolutional Neural Network with Wide Multi-Layer Kernels and Its Application

When the quantity of parameters or values in systems being tested is substantial, there is a significant escalation in the number of combinatorial test cases. The execution of all test cases will require a substantial allocation of time and resources. Prioritization technology enables early detection of system faults and enhances test efficiency. Most existing prioritization methods rely on historical empirical data, which can be challenging to obtain in many cases. In the meantime, random prioritization often leads to lower fault detection rates. This paper presents a prioritization method for combinatorial test cases based on a convolutional neural network (CNN) model. The weights of suspected fault-inducing interactions are initially extracted through a convolution operation in the subset of upfront test cases. Second, the features related to fault-inducing interactions are derived using wide multi-layer kernels convolutional neural network (WCNN). Third, the deep WCNN-SVM model undergoes training and makes predictions on the entire set of test cases. The predicted results are then combined with weights to prioritize combinatorial test cases. Test cases of equal priority are adjusted based on distance entropy. Application experiments on UAV demonstrate that the proposed method effectively enhances both fault detection speed and fault detection rate.