The generated HTTP traffic of users' interactions with a Web application can be logged for further analysis. In this thesis, we present the ``Session Reconstruction'' problem that is the reconstruction of user interactions from recorded request/response logs of a session. The reconstruction is especially useful when the only available information about the session is its HTTP trace, as could be the case during a forensic analysis of an attack on a website. New Web technologies such as AJAX and DOM manipulation have provided more responsive and smoother Web applications, sometimes called ``Rich Internet Applications''(RIAs). Despite the benefits of RIAs, the previous session reconstruction methods for traditional Web applications are not effective anymore. Recovering information from a log in RIAs is significantly more challenging as compared with classical Web applications, because the HTTP traffic contains often only application data and no obvious clues about what the user did to trigger that traffic. This thesis studies applying different techniques for efficient reconstruction of RIA sessions. We define the problem in the context of the client/server applications, and propose a solution for it. We present different algorithms to make the session reconstruction possible in practice: learning mechanisms to guide the session reconstruction process efficiently, techniques for recovering user-inputs and handling client-side randomness, and also algorithms for detections of actions that do not generate any HTTP traffic. In addition, to further reduce the session reconstruction time, we propose a distributed architecture to concurrently reconstruct a RIA session over several nodes. To measure the effectiveness of our proposed algorithms, a prototype called D-ForenRIA is implemented. The prototype is made of a proxy and a set of browsers. Browsers are responsible for trying candidate actions on each state, and the proxy, which contains the observed HTTP trace, is responsible for responding to browsers' requests and validating attempted actions on each state. We have used this tool to measure the effectiveness of the proposed techniques during session reconstruction process. The results of our evaluation on several RIAs show that the proposed solution can efficiently reconstruct use-sessions in practice.

The testing of software for robot controllers has become increasingly difficult as robotic systems become more complex. As the complexity of the systems increases, the number of hardware systems that the robot relies on also grows. This poses a challenge in testing robot controllers, which is crucial to ensure that robots function safely and effectively in their intended applications. While simulation can be used as a platform for software testing, it is not feasible to simulate everything in a virtual environment, especially when test cases require physical connections to hardware for input and output signals sent to robot controllers. Therefore, the objective of this thesis is to replicate I/O device network communication in order to enhance virtual testing processes. The approach employed involves capturing real-time network traffic, modifying and rebuilding it, and subsequently replaying it. The work examines existing academic research on these approaches and technologies, and investigates the specific challenges in the testing process by conducting research within a company leading globally in industrial robot development. A conceptual model is proposed, and a prototype is developed. The solution demonstrates potential in addressing the current challenges in robot controller testing by enabling network capture, modification, and level 4 network traffic replay. However, experimental results reveal various limitations, such as significant delays in generating responses. Therefore, further research and development are required if the solution is to be implemented in a real-world setting.