Analysing Reverse Engineering Techniques for Interactive Systems

Reverse engineering is the process of discovering a model of a software system by analyzing its structure and functions. Reverse engineering techniques applied to interactive software applications (e.g. applications with user interfaces (UIs)) are very important and significant, as they can help engineers to detect defects in the software and then improve or complete them. There are several approaches, and many different tools, which are able to reverse-engineer software applications into formal models. These can be classified into two main types: dynamic tools and static tools. Dynamic tools interact with the application to find out the run-time behaviours of the software, simulating the actions of a user to explore the system’s state space, whereas static tools focus on static structure and architecture by analysing the code and documents. Reverse engineering techniques are not common for interactive software systems, but nowadays more and more organizations recognize the importance of interactive systems, as the trend in software used in computers is for applications with graphical user interfaces. This has in turn led to a developing interest in reverse engineering tools for such systems. Many reverse engineering tools generate very big models which make analysis slow and resource intensive. The reason for this is the large amount of information that is generated by the existing reverse engineering techniques. Slicing is one possible technique which helps with reducing un-necessary information for building models of software systems. This project focuses on static analysis and slicing, and considers how they can aid reverse engineering techniques for interactive systems, particularly with respect to the generation of a particular set of models, Presentation Models (PModels) and Presentation Interaction Models (PIMs)

Multi-Camera View Based Proactive BS Selection and Beam Switching for V2X

Due to the short wavelength and large attenuation of millimeter-wave (mmWave), mmWave BSs are densely distributed and require beamforming with high directivity. When the user moves out of the coverage of the current BS or is severely blocked, the mmWave BS must be switched to ensure the communication quality. In this paper, we proposed a multi-camera view based proactive BS selection and beam switching that can predict the optimal BS of the user in the future frame and switch the corresponding beam pair. Specifically, we extract the features of multi-camera view images and a small part of channel state information (CSI) in historical frames, and dynamically adjust the weight of each modality feature. Then we design a multi-task learning module to guide the network to better understand the main task, thereby enhancing the accuracy and the robustness of BS selection and beam switching. Using the outputs of all tasks, a prior knowledge based fine tuning network is designed to further increase the BS switching accuracy. After the optimal BS is obtained, a beam pair switching network is proposed to directly predict the optimal beam pair of the corresponding BS. Simulation results in an outdoor intersection environment show the superior performance of our proposed solution under several metrics such as predicting accuracy, achievable rate, harmonic mean of precision and recall