Regression testing repeatedly executes test cases of previous builds to validate that the original features are not affected with any new changes. In recent years, regression testing has seen a remarkable progress with the increasing popularity of agile methods, which stress the central role of regression testing in maintaining software quality. The optimum case for regression testing in agile context is to run regression set at the end of each sprint and release, which requires a lot of cost and time. In this paper, we present an automated agile regression testing approach on both the sprints and release levels. The proposed approach addresses both weighted sprint test cases prioritization technique, which prioritizes test cases based on several parameters having real practical weight for testers, and Cluster‐based Release Test cases Selection technique that clusters user stories based on the similarity of covered modules to solve the scalability issue. Test cases are then selected based on issues logged for failed test cases using text mining techniques. The proposed approach achieves enhancement for both the prioritization and selection of test cases for agile regression testing. Copyright © 2016 John Wiley & Sons, Ltd.

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During the Coronavirus pandemic, e-learning systems have proven to be an essential pillar for education. This raises to surface what many studies have addressed earlier; creating a platform that completes the traditional classroom work and maximizes the effectiveness of learning outcomes. Striving to achieve such platform, studies have considered gamifying and personalizing the educational resources for the adaptation of educational systems as per the intended learners through intensive learning analytics. But was the learner really a part of the adaptation process taking place? Learning analytics are usually designed to the course's adaptation and solely for the teachers. Thus, learning analytics in gamified adaptive educational systems involving the course, teachers and learners together are still under investigation. In this study, the Personalized Adaptive Gamified E-learning (PAGE) model is introduced to extend MOOCs by providing new satisfactory levels of learning analytics and visualization in the rich e-learning process that supports the learner's intervention in the resultant learning analytics. The proposed Learning analytics have been developed to make the necessary adaptation to the course and learner's learning flow, as well as visualizing the process and adaptation decisions to the learners. Results show a positive potential towards learning adaptation and visualization, and a necessity to provide an additional focus for the gamification concept.

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Tremendous systems are rapidly evolving based on the trendy Internet of Things (IoT) in various domains. Different technologies are used for communication between the massive connected devices through all layers of the IoT system, causing many security and performance issues. Regression and integration testing are considered repeatedly, in which the vast costs and efforts associated with the frequent execution of these inflated test suites hinder the adequate testing of such systems. This necessitates the focus on exploring innovative scalable testing approaches for large test suites in IoT-based systems. In this paper, a scalable framework for continuous integration and regression testing in IoT-based systems (IoT-CIRTF) is proposed, based on IoT-related criteria for test case prioritization and selection. The framework utilizes search-based techniques to provide an optimized prioritized set of test cases to select from. The selection is based on a trained prediction model for IoT standard components using supervised deep learning algorithms to continuously ensure the overall reliability of IoT-based systems. The experiments are held on two GSM datasets. The experimental results achieved prioritization accuracy up to 90% and 92% for regression testing and integration testing respectively. This provides an enhanced and efficient framework for continuous testing of IoT-based systems, as per IoT-related criteria for the prioritization and selection purposes.

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Advances in location acquisition and mobile technologies led to the addition of the location dimension to Social Networks (SNs) and to the emergence of a newer class called Location-Based Social Networks (LBSNs). While LBSNs are richer in their model and functions than SNs, they fail so far to attract as many users as SNs. On the other hand, SNs have large amounts of geo-tagged media that are under-utilized. In this paper, we propose an Interest-Aware Location-Based Recommender system (IALBR), which combines the advantages of both LBSNs and SNs, in order to provide interest-aware location-based recommendations. This recommender system is proposed as an extension to LBSNs. It is novel in: (1) utilizing the geo-content in both LBSNs and SNs; (2) ranking the recommendations based on a novel scoring method that maps to the user interests. It also works for passive users who are not active content contributors to the LBSN. This feature is critical to increase the number of LBSN users. Moreover, it helps with reducing the cold start problem, which is a common problem facing the new users of recommender systems who get random unsatisfying recommendations. This is due to the lack of user interest awareness, which is reliant on user history in most of the recommenders. We evaluated our system with a large-scale real dataset collected from foursquare with respect to precision, recall and the f-measure. We also compared the results with a ground truth system using metrics like the normalized discounted cumulative gain and the mean absolute error. The results confirm that the proposed IALBR generates more efficient recommendations than baselines in terms of interest awareness.

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