Continuous change changes everything; it introduces various uncertainties, which may harm the sustainability of software systems. We argue that integrating runtime adaptation and evolution is crucial for the sustainability of software systems. Realising this integration calls for a radical change in the way software is developed and operated. Our position is that we need to Design for Sustainability. To that end, we present: (i) the AdEpS model (Adaptation and Evolution processes for Sustainability) to handle and mitigate uncertainties by means of integrating runtime adaptation and evolution, and (ii) a set of engineering principles to design software systems that facilitate the application of the AdEpS model to build sustainable software.
The use of artificial intelligence and machine learning techniques across all disciplines has exploded in the past few years, with the ever-growing size of data and the changing needs of higher education, such as digital education. Similarly, online educational information systems have a huge amount of data related to students in digital education. This educational data can be used with artificial intelligence and machine learning techniques to improve digital education. This study makes two main contributions. First, the study follows a repeatable and objective process of exploring the literature. Second, the study outlines and explains the literature’s themes related to the use of AI-based algorithms in digital education. The study findings present six themes related to the use of machines in digital education. The synthesized evidence in this study suggests that machine learning and deep learning algorithms are used in several themes of digital learning. These themes include using intelligent tutors, dropout predictions, performance predictions, adaptive and predictive learning and learning styles, analytics and group-based learning, and automation. artificial neural network and support vector machine algorithms appear to be utilized among all the identified themes, followed by random forest, decision tree, naive Bayes, and logistic regression algorithms.
This paper presents the design, implementation and the overall lifecycle of a software system that includes mobile and web components and that evolved having the following aspects in mind: (1) System Requirements and Architectural Design, (2) System Implementation and Deployment, and (3) System Assessment and Usability Testing. During the three years of development efforts three software prototypes were implemented utilizing service-oriented approaches. These efforts have been tested with more than 200 users during this period. The outcomes of these activities led to the design and implementation of a system architecture that relies on serviceoriented approaches and open standards. Moreover, extensive prototyping with incremental development stages helped to find the balance between the design and implementation of the system while reflecting to rapid changes of software and webbased technologies. Finally, user testing for assessment and testing of the software system were employed in order to cope with the dynamic user requirements. The main outcomes of the efforts described in this paper are presented and summarized in the form of Architectural Concepts that pave the way towards an open, extensible architecture.
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