Test case generation is a core phase in any testing process, therefore automating it plays a tremendous role in reducing the time and effort spent during the testing process.This paper proposes an enhanced XML-based automated approach for generating test cases from activity diagrams. The proposed architecture creates a special table called Activity Dependency Table (ADT) for each XML file. The ADT covers all the functionalities in the activity diagram as well as handling the decisions, loops, fork, join, merge, object and conditional threads.Then it automatically generates a directed graph called Activity Dependency Graph (ADG) that is used in conjunction with the ADT to extract all the possible final test cases. The proposed model validates the generated test paths during the generation process to ensure meeting a hybrid coverage criterion. The generated test cases can be sent to any requirements management tool to be traced against the requirements.The proposed model is prototyped on 30 differently sized activity diagrams in different domains An experimental evaluation of the proposed model is done as well. It saves time and effort besides, increases the quality of generated test cases, therefore optimizes the overall performance of the testing process Moreover, the generated test cases can be executed on the system under test using any automatic test execution tool.
The Internet of Medical Things was immensely implemented in healthcare systems during the covid 19 pandemic to enhance the patient's circumstances remotely in critical care units while keeping the medical staff safe from being infected. However, Healthcare systems were severely affected by ransomware attacks that may override data or lock systems from caregivers' access. In this work, after obtaining the required approval, we have got a real medical dataset from actual critical care units. For the sake of research, a portion of data was used, transformed, and manifested using laboratory-made payload ransomware and successfully labeled. The detection mechanism adopted supervised machine learning techniques of K Nearest Neighbor, Support Vector Machine, Decision Trees, Random Forest, and Logistic Regression in contrast with deep learning technique of Artificial Neural Network. The methods of KNN, SVM, and DT successfully detected ransomware's signature with an accuracy of 100%. However, ANN detected the signature with an accuracy of 99.9%. The results of this work were validated using precision, recall, and f1 score metrics.
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