Software Coupling and Cohesion Model for Measuring the Quality of Software Components

Measuring software quality requires software engineers to understand the system’s quality attributes and their measurements.The quality attribute is a qualitative property;however,the quantitative feature is needed for software measurement,which is not considered during the development of most software systems.Many research studies have investigated different approaches for measuring software quality,but with no practical approaches to quantify and measure quality attributes.This paper proposes a software quality measurement model,based on a software interconnection model,to measure the quality of software components and the overall quality of the software system.Unlike most of the existing approaches,the proposed approach can be applied at the early stages of software development,to different architectural design models,and at different levels of system decomposition.This article introduces a software measurement model that uses a heuristic normalization of the software’s internal quality attributes,i.e.,coupling and cohesion,for software quality measurement.In this model,the quality of a software component is measured based on its internal strength and the coupling it exhibits with other component(s).The proposed model has been experimented with nine software engineering teams that have agreed to participate in the experiment during the development of their different software systems.The experiments have shown that coupling reduces the internal strength of the coupled components by the amount of coupling they exhibit,which degrades their quality and the overall quality of the software system.The introduced model can help in understanding the quality of software design.In addition,it identifies the locations in software design that exhibit unnecessary couplings that degrade the quality of the software systems,which can be eliminated.

Framework Development Using Data Mining Techniques to Predict Mortality Risk during Pandemic

The corona virus, which causes the respiratory infection Covid-19, was first detected in late 2019. It then spread quickly across the globe in the first months of 2020, reaching more than 15 million confirmed cases by the second half of July. This global impact of the novel coronavirus (COVID-19) requires accurate forecasting about the spread of confirmed cases as well as continuation of analysis of the number of deaths and recoveries. Forecasting requires a huge amount of data. At the same time, forecasts are highly influenced by the reliability of the data, vested interests, and what variables are being predicted. Again, human behavior plays an important role in efficiently controling the spread of novel coronavirus. This paper introduces a sustainable approach for predicting the mortality risk during the pandemic to help medical decision making and raise public health awareness. This paper describes the range of symptoms for corona virus suffered patients and the ways of predicting patient mortality rate based on their symptoms.