An investigation of requirements traceability practices in software companies in Malaysia

Requirement traceability (RT) is one of the critical activity of good requirements management and an important part of development projects. At the same time, it improves the quality of software products. Nevertheless, industrial practitioners are challenged by this lack of guidance or results which serve as a rule or guide in establishing effective traceability in their projects. The outcome of this is that practitioners are ill-informed as to the best or most efficient means of accomplishing their tasks, such as found in software companies. Notwithstanding the lack of guidance, there are a number of commonly accepted practices which can guide industrial practitioners with respect to trace the requirements in their projects. This study aims to determine the practices of RT through conducting a systematic literature review. Also, this study conducted a survey for investigating the use of RT practices in the software companies at northern region of Malaysia. Finally, a series of interviews with practitioners were carried out to know the reasons that influence on the use of these practices in software development. The findings showed that majority software companies do not use traceability practices for tracing requirements due to financial issues and the lack of knowledge of these practices. This study presented empirical evidence about the use of RT practices among software companies. Thus, the findings of this study can assist practitioners to select RT practices, and also enables researchers to find gaps and pointers for future study in this study domain

Machine Learning Algorithms in Analysis, Diagnosing and Predicting COVID-19: A Systematic Literature Review

Since the COVID-19 corona virus first appeared at the end of 2019, in Wuhan province, China, the analysis, diagnosis, and prognosis of COVID-19 (SARS-CoV-2) has attracted the greatest attention. Since then, every part of the world needs some sort of system or instrument to assist judgments for prompt quarantine and medical treatment. For a variety of uses, including prediction, classification, and analysis, machine learning (MLR) have demonstrated their accuracy and efficiency in the fields of education, health, and security. In this paper, three main questions will be answered related to COVID-19 analysis, predicting, and diagnosing. The performance evaluation, fast process and identification, quick learning, and accurate results of MLR algorithms make them as a base for all models in analyzing, diagnosing, and predicting COVID-19 infection. The impact of using supervised and unsupervised MLR can be used for estimating the spread level of COVID-19 to make the proper strategic decisions. The researchers next compared the effects of various datatypes on diagnosing, forecasting, and assessing the severity of COVID-19 infection in order to examine the effects of MLRs. Three fields are associated with COVID-19, according to the analysis of the chosen study (analysis, diagnosing, and predicting). The majority of researches focus on the subject of COVID-19 diagnosis, where they use their models to identify the infection. In the selected studies, several algorithms are employed, however, a study revealed that the neural network is the most used method when compared to other algorithms. The most used method for identifying, forecasting, and evaluating COVID-19 infection is supervised MLR

Supervised Learning Algorithms in Educational Data Mining: A Systematic Review

The academic institutions always looking for tools that improve their performance and enhance individuals outcomes. Due to the huge ability of data mining to explore hidden patterns and trends in the data, many researchers paid attention to Educational Data Mining (EDM) in the last decade. This field explores different types of data using different algorithms to extract knowledge that supports decision-making and academic sector development. The researchers in the field of EDM have proposed and adopted different algorithms in various directions. In this review, we have explored the published papers between 2010-2020 in the libraries (IEEE, ACM, Science Direct, and Springer) in the field of EDM are to answer review questions. We aimed to find the most used algorithm by researchers in the field of supervised machine learning in the period of 2010-2020. Additionally, we explored the most direction in the EDM and the interest of the researchers. During our research and analysis, many limitations have been examined and in addition to answering the review questions, some future works have been presented