Automated Test Case Generation for Path Coverage （ATCG-PC） problem is a hot topic in the field of automated software testing. The fitness functions commonly used by swarm intelligence evolutionary algorithms in ATCG-PC problem are highly similar with each other， but the existing swarm intelligence evolutionary algorithms for solving ATCG-PC problem do not consider this similarity feature yet. Inspired by the similarity feature， the two similar fitness functions were treated as two tasks， so that ATCG-PC problem was transformed into a multi-task ATCG-PC problem， and a new swarm intelligence evolutionary algorithm called Multifactorial Backtracking Search optimization Algorithm （MFBSA） was proposed to solve multi-task ATCG-PC problem. In the proposed algorithm， the memory population function of multifactorial selection Ⅰ was used to improve the global search ability， and the similar tasks were able to improve each other’s optimization efficiency through knowledge transfer by assortative memory mating. The performance of MFBSA was evaluated on six fog computing test programs and six natural language processing test programs. Compared with Backtracking Search optimization Algorithm （BSA）， Immune Genetic Algorithm （IGA）， Particle Swarm Optimization with Convergence Speed Controller （PSO-CSC） algorithm， Adaptive Particle Swarm Optimization （APSO） algorithm and Differential Evolution with Hypercube-based learning strategies （DE-H） algorithm， MFBSA has the total test cases used to cover the paths on 12 test programs reduced by 64.46%， 66.64%， 67.99%， 74.15%， and 61.97%， respectively. Experimental results show that the proposed algorithm can effectively reduce testing cost.

Face verification is widely used in various scenes in life， and the acquisition of ordinary RGB images is extremely dependent on illumination conditions. In order to solve the interference of illumination and head pose， a convolutional neural network based Siamese network L2-Siamese was proposed. Firstly， the paired depth images were taken as input. Then， after using two convolutional neural networks that share weights to extract facial features respectively， L2 norm was introduced to constrain the facial features with different poses on a hypersphere with a fixed radius. Finally， the fully connected layer was used to map the difference between the features to the probability value in （0，1） to determine whether the group of images belonged to the same object. In order to verify the effectiveness of L2-Siamese， a test was conducted on the public dataset Pandora. Experimental results show that L2-Siamese has good overall performance. After the dataset was grouped according to the size of head pose interference， the test results show that the prediction accuracy of L2-Siamese is 4 percentage points higher than that of the state-of-the-art algorithm fully-convolutional Siamese network under the maximum head pose interference， illustrating that the accuracy of prediction has been significantly improved.