Ziad A. Al Sharif

Due to the incremental and iterative nature of the software testing process, a test suite may become bloated with redundant, overlapping, and similar test cases. This paper aims to optimize a bloated test suite by employing an execution trace that encodes runtime events into a sequence of characters forming a string. A dataset of strings, each of which represents the code coverage and execution behavior of a test case, is analyzed to identify similarities between test cases. This facilitates the de-bloating process by providing a formal mechanism to identify, remove, and reduce extra test cases without compromising software quality. This form of analysis allows for the clustering and classification of test cases based on their code coverage and similarity score. This paper explores three levels of execution traces and evaluates different techniques to measure their similarities. Test cases with the same code coverage should generate the exact string representation of runtime events. Various string similarity metrics are assessed to find the similarity score, which is used to classify, detect, and rank test cases accordingly. Additionally, this paper demonstrates the validity of the approach with two case studies. The first shows how to classify the execution behavior of various test cases, which can provide insight into each test case’s internal behavior. The second shows how to identify similar test cases based on their code coverage.

The rapid development in the digital world has contributed to the dramatic increase in the number of cybercrimes. An application's volatile data that is kept in memory (RAM) could give clues on how a criminal has been using the application up to acquisition time. Unfortunately, application-level memory forensics has been conducted in an ad hoc manner because a forensic investigator has to come up with a new technique for a new application. This process has become problematic and exhausting. This paper proposes a general solution to investigate any application in memory. We heavily utilize applications' debugging information generated by compilers in our solution. Furthermore, we extend Volatility [Walters, 2007], an open-source memory forensic framework, by developing and integrating a plugin to investigate applications in memory. We design several experiments to evaluate the effectiveness of our plugin. Interestingly, our plugin can parse debugging information and extract variables' names and data types regardless of their scope and complexity. In addition, we experimented with a real world application and succeeded in collecting vital information out of it. By accurately computing the Virtual Addresses (VA) of variables along with their allocated memory sizes based on their types, we are able to extract their values out of memory. In addition, we trace call stacks as per threads to extract local variables' values. Finally, direct and indirect pointers are successfully dereferenced.

The success of applying deep learning to many domains has gained strong interest in developing new revolutionary recommender systems. However, there are little works studying these systems that employ deep learning; additionally, there is no study showing how to combine the users and items embedding with deep learning to enhance the effectiveness of the recommender systems. Therefore, this paper proposes a novel approach called RecDNNing with a combination of embedded users and items combined with deep neural network. The proposed recommendation approach consists of two phases. In the first phase, we create a dens numeric representation for each user and item, called user embedding and item embedding, respectively. Following that, the items and users embedding are averaged and then concatenated before being fed into the deep neural network. In the second phase, we use the model of the deep neural network to take the concatenated users and items embedding as the inputs in order to predict the scores of rating by applying the forward propagation algorithm. The experimental results on MovieLens show that the proposed RecDNNing outperforms state-of-the-art algorithms.