With the widespread use of mobile devices and emerging mobile applications， the exponential growth of traffic in mobile networks has caused problems such as network congestion， large delay， and poor user experience that cannot satisfy the needs of mobile users. Edge caching technology can greatly relieve the transmission pressure of wireless networks through the reuse of hot contents in the network. At the same time， it has become one of the key technologies in 5G/Beyond 5G Mobile Edge Computing （MEC） to reduce the network delay of user requests and thus improve the network experience of users. Focusing on mobile edge caching technology， firstly， the application scenarios， main characteristics， execution process， and evaluation indicators of mobile edge caching were introduced. Secondly， the edge caching strategies with energy efficiency， delay， hit ratio， and revenue maximization as optimization goals were analyzed and compared， and their key research points were summarized. Thirdly， the deployment of the MEC servers supporting 5G was described， based on this， the green mobility-aware caching strategy in 5G network and the caching strategy in 5G heterogeneous cellular network were analyzed. Finally， the research challenges and future development directions of edge caching strategies were discussed from the aspects of security， mobility-aware caching， edge caching based on reinforcement learning and federated learning and edge caching for Beyond 5G/6G networks.

The activities of the programmers including copy, paste and modify result in a lot of code clone in the software systems. However, the inconsistent change of code clone is the main reason that causes program error and increases maintenance costs in the evolutionary process of the software version. To solve this problem, a new research method was proposed. The mapping relationship between the clone groups was built at first. Then the theme of lineal cloning cluster was extracted using Latent Dirichlet Allocation (LDA) model. Finally, the inconsistent change probability of code clone was predicted. A software which contains eight versions was tested and an obvious discrimination was got. The experimental results show that the method can effectively predict the probability of inconsistent change and be used for evaluating quality and credibility of software.