Software program testing is the procedure of exercising a software component with a selected set of test cases as a way to discover defects and assess quality. Using software testing automation, especially the generating of testing data increases the effectiveness and efficiency of software testing as a whole. Instead of creating testing data from scratch, Big Data (BD) offers an important source of testing data. Although it is a good source, there is a need to select a proper set of testing data for the sake of selecting an optimal sub-domain input values from the BD. To refine the efficiency of software testing, this paper proposes a hybrid Genetic Algorithm and Binary Search (BSGA) technique that is used for detecting the error-prone path in a program. The BSGA combines the Genetic Algorithm (GA) with the Binary Search (BS) algorithm that uses the BD as input values for the program path coverage, and thus enhances the software testing. The BSGA represents a robust nonlinear search technique and a better quality solution, which therefore results in a cost reduction in the software testing industry. The experiments show that the results approved the impact of using the BS to enhance the performance of the GA, in terms of finding optimal test cases and test data for the input Big Data domain values. Whereas, these results minimize the cost of testing.

Data exists in large volume in the modern world, it becomes very useful when decoded correctly to inform decision making towards tackling real word issues. However, when the data is conflicting, it becomes a daunting task to get obtain information. Working on missing data has become a very impor-tant task in big data analysis. This paper considers the handling of the missing data using the Support Vector Machine (SVM) based on a technique called Correlation-Genetic Algorithm-SVM. This data is to be subjected to the SVM classification technique after identifying the attribute’s correlation and application of the genetic algorithm. The application of the correlation enables a clear view of the attributes which are highly correlated within a particular dataset. The results indicate that apart from the SVM, the application of the proposed hybrid algorithm produces better outcomes identification rate and accuracy is considered. The proposed approach is also compared with depicts the Mean Identification rate of applying the neural network, the result indicate a consistent accuracy hence making it better.

This study aimed to evaluate the effectiveness of Moodle LMS as an alternative delivery mode for education, particularly in the higher education system of Jordan, which has not been evaluated before. The research is based on Moore’s transactional distance learning theory, which considers interactions among students, instructors, and course material, as well as personal characteristics of students. The study included 50 participants who have taken courses on Moodle LMS at the University of Jordan. Both interviews and research surveys were conducted to gather in-depth perceptions and analysis of the participants’ experiences with Moodle LMS. The findings suggest that Moodle LMS was an effective alternative study tool during the COVID-19 pandemic and the user-friendly course design enhanced the user experience of using Moodle LMS. Participants also provided suggestions to optimize the system for better integration in the University of Jordan. Fixing Moodle LMS based on the recommendations of the study participants will significantly improve the learning experience of students. This study contributes to the advancement of the state of the art by evaluating the effectiveness of Moodle LMS in the higher education system of Jordan and providing recommendations for improvement.

A critical step in the creation of software is the elicitation of requirements. According to most of the research, nonfunctional requirements get less attention from nonfunctional requirements, also necessary for the creation of every new application. A poor choice of elicitation causes the system to malfunction. Without the use of an elicitation approach, the needs and requirements of users cannot be ascertained. Ensuring efficient communication between analysts and users during the elicitation process is the biggest challenge for analysts. This study’s major artifact is a proposed model that creates a conceptual model automatically from a series of agile requirements given as user stories. The accuracy, especially when user stories are succinct assertions that identify the issue to be handled, was one of our case study’s positive outcomes. The objective was to prove that artificial intelligence can be used to elicit software requirements for software systems, and the findings support this claim. An elicitation model for NFR in agile methodology is proposed by this work. The approach will help the software business identify and collect needs for all kinds of software. as well as directing both users and developers in the development of software. Due to the elicitations of both FRs and NFRs of the initial phase in agile projects, which receive less attention, this study decreased the time, effort, and risk.