Coronavirus or what has been termed COVID-19 is one of the infectious diseases that have been recently classified as a pandemic. Currently, it is considered as the activist and the most dangerous disease that is rapidly spreaded around the world causing thousands of death cases. COVID-19 spreads between people through the contact with the infected ones when they sneeze, cough, or droplets of saliva. In this article, we investigated the impact of the spatial aspects and the movement patterns on COVID-19 infection spreading. We considered three aspects, namely, mobility patterns, curfew (stay-at-home) impact, and the distribution of people within places. The results show that spatial aspects can be considered as one of the factors that play a significant role in spreading the virus.
Recent years have witnessed a great revolution in software applications. The quality of the software is important insofar as it contributes to providing better services for users. Software metrics are mainly used to obtain feedback on the quality of software design. These metrics enable developers to identify the potential weaknesses of their designs. Furthermore, software metrics may have correlations with each other and impact the outcome of each other. This specific case may cause a misleading interpretation of the design, which eventually affects the quality of software and waste the time and effort during the design phase. Therefore, selecting the appropriate metric during this phase should be carefully performed. In this work, we use network science concepts for deeply investigating the relations among object-oriented software metrics. The analysis approach is based on network visualization and network measurements. The dataset of this work was collected from accredited references in the field of Software Engineering. This study involves the main 104 metrics that are basically used during software design. The findings demonstrated interesting facts on the relations among metrics. Besides, this work is considered as a comprehensive analysis and assessment that takes into account different dimensions of the relations among software metrics. We believe that the analysis and the results can make it easy for developers in selecting the appropriate metrics during the design phase.
Software design is one of the very important phases of the software engineering. The costs of software can be minimized if improvements or corrections made during this stage. Several of the current computer aided software engineering (CASE) tools like enterprise architect (EA) v12 do not have the capability to improve the design. This work aims to develop an algorithm that helps the software engineers evaluating the design quality utilizing one of the object-oriented (OO) design models namely quality metrics for object-oriented design (QMOOD) which represents as hierarchical model that describes the relationship between quality attributes such as reusability, extendibility and properties of the design of OO design. This algorithm describesed how the assessment of the extendibility/ extensibility using the software metrics has been done and the impact of the involved metrics in the extendibility value. Results obtained demonstrate the effect of OO design metrics such as inheritance, polymorphism, abstraction and coupling in quality characteristics like extensibility. The results show that lower values of abstraction and coupling, obtain higher value of extendibility which means the class diagram is ready to accept additional improvements. The proposed algorithm has been tested on two different systems (test cases) that vary in their class diagrams, functionalities, and complexities.
Recent years have witnessed a great revolution in web technologies and their applications. Most of these applications are connected to the Internet. One of the most frequent issues in these applications is the security issue. Malware is the main reason behind this issue since they harm users in many different aspects such as damaging files, stealing credentials, operating system malfunctioning, etc. Therefore, many companies around the world develop antiviruses software aiming to mitigate the security issue. Most of the known viruses can access users’ computers or web accounts through some APIs. Therefore, antivirus companies try to update the API databases of their software periodically. This paper suggests a method for investigating the relations among different kinds of malware in terms of the API they used. Then, it provides recommendations about this malware and its APIs. The method followed in this work is based on concepts inspired by network science. The malware and its APIs are modeled as a network with nodes and edges. The results show interesting facts about the investigated malware that are of interest for software security architects and give the relations between various malware which call the same API function, depending on that malicious software behavior can be detected by antivirus or anti-malware engine.
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