Software security testing technology has become an essential method for software developers to improve software performance and resist network attacks in the Internet age. DevSecOps （Development， Security and Operations）， as a new generation software development pattern which integrates Security and Operations into Development and maintenance， can identify the possible threats to the software and effectively evaluate the security of software， and can make software security risks within control. Therefore， starting from the process of DevOps （Development and Operations）， the various stages of DevOps involving software security testing techniques were sorted out， including source code audit， fuzzing， vulnerability scanning， penetration testing， and security crowdsourced testing techniques. And by collecting and analyzing the relevant technical literature in the last three years in well-known index databases， such as SCI， EI， SCOPUS， CNKI， CSCD and WanFang， the research status of the above techniques was summarized and the recommendations for the use of relevant testing tools were given. At the same time， aiming at the advantages and disadvantages of each technical support means， the future development directions of software development mode DevSecOps were prospected.

The relatively large time cost of the existing influence algorithms does not fit the social networks of which the scale keeps expanding. An influence optimization model was proposed based on the network community structure for solving problem of large time cost. Firstly evaluate nodes influence in each community and dig core members, and then find a small subset of nodes in the set composed of the core nodes and linking community nodes to get the maximization diffusion with minimization cost. The experimental results demonstrate that our model achieves the subset with more abroad influence diffusion and reduces running time compared with traditional methods. Its influence coverage is up to 90%.

Since the soft shadow achieved by the existing shadow generation algorithms of Augmented Reality (AR) is unrealistic, the authors proposed a shadow generation algorithm using adaptive sampling and background fusion. First, the authors computed shadow spatial location distribution of virtual objects by using planar shadow algorithm which took occlusion into account. Then, to improve the procedure of soft shadow generation in swell and erode algorithm, an adaptive sampling method which got illuminant union according to shape types was presented. Finally, since shadow color gotten by gray image method was limited to single channel, the authors presented a method based on multi-channel and background fusion. The experimental results show that in the proposed algorithm the color of soft shadow is more reasonable and the method of soft shadow rendering is more effective. Consequently, the presented algorithm improves the realism of soft shadow.