To reduce iteration and maintenance time of Digital Twin （DT） systems as well as evolution cost of DT systems， the potential of integrating Development and Operations （DevOps） methodology into DT systems was explored， and an innovative DT system DevOps practice DT Operations （DTOps） was proposed. A service-oriented system architecture was designed for the specific needs and characteristics of DT systems， thereby enhancing scalability and agility of system， and the infrastructure of DTOps as well as the implementation methods of Continuous Integration （CI） and Continuous Delivery （CD） were provided. In a case study of a gear production line， various stages of DTOps were validated using open-source tools， demonstrating feasibility and convenience of DTOps. Experimental results show that DTOps improves the evolution efficiency by 29.7% and 26.9% compared to monolithic and microservice architectures， respectively. This effect is particularly significant in highly integrated and data-intensive environments， confirming the effectiveness of DTOps in engineering applications.

The existing deep learning-based methods for source code vulnerability detection often suffer from severe loss of syntax and semantics in target code， and neural network models allocating weights to the graph nodes （edges） in target code unreasonably. To address these issues， a method named VulATGCN for detecting source code vulnerabilities was proposed on the basis of Code Property Graph （CPG） and Adaptive Transformer-Graph Convolutional Network （AT-GCN）. In the method， CPG was used to represent source code， CodeBERT was combined for node vectorization， and graph centrality analysis was employed to extract deep structural features， thereby capturing the code’s syntax and semantic information in multi-dimensional way. After that， AT-GCN model was designed by integrating strengths of Transformer-based self-attention mechanism， which excels at capturing long-range dependencies， and Graph Convolutional Network （GCN）， which is proficient at capturing local features， thereby realizing fusion learning and precise extraction of features from regions with different importance. Experimental results on real vulnerability datasets Big-Vul and SARD show that the proposed method VulATGCN achieves an average F1 score of 82.9%， which is 10.4% to 132.9% higher than deep learning-based vulnerability detection methods such as VulSniper， VulMPFF， and MGVD， with an average increase of approximately 52.9%.

Code reuse not only brings convenience to software development， but also introduces security risks， such as accelerating vulnerability propagation and malicious code plagiarism. Code similarity detection technology is to calculate code similarity by analyzing lexical， syntactic， semantic and other information between codes. It is one of the most effective technologies to judge code reuse， and it is also a program security analysis technology that has developed rapidly in recent years. First， the latest technical progress of code similarity detection was systematically reviewed， and the current code similarity detection technology was classified. According to whether the target code was open source， it was divided into source code similarity detection and binary code similarity detection. According to the different programming languages and instruction sets， the second subdivision was carried out. Then， the ideas and research results of each technology were summarized， the successful cases of machine learning technology in the field of code similarity detection were analyzed， and the advantages and disadvantages of existing technologies were discussed. Finally， the development trend of code similarity detection technology was given to provide reference for relevant researchers.

In the electronic whiteboard system based on machine vision, an improved mean filter was proposed to eliminate touching-point jitter. In order to enhance the working fluency without hardware restrictions,a coordinate interpolation based on curve-fitting was adopted to improve the real-time performance of the whole system and smooth the trajectory of moving touching-point. The experimental results show that: on one hand, touching-point jitter can be eliminated. On the other hand, the system can output 180 touching-point coordinates per second when the camera works at its highest speed of 60 frame per second. The real-time performance of the whole system gets effectively improved without any new hardware cost.