In education systems, learners face the challenge of selecting appropriate courses that align with their academic goals or training needs. This paper introduces a hybrid course recommendation system that combines collaborative filtering and knowledge graphs to provide personalised and explainable course suggestions that guide students through their enrollment process. Unlike traditional recommendation methods, which often lack transparency and may offer unsuitable courses for students with different enrollment needs, our system helps prevent redundant suggestions and ensures that students receive appropriate course options based on their preferences and interactions. The system uses a Bernoulli matrix factorisation model to predict the course status, and a filtering method through information extracted from a knowledge graph to provide students with an effective and explainable recommendation. Empirical evaluation of a real-world dataset of 36.6K student records indicates that the proposed system achieves slightly higher levels of precision, recall and F1-score compared to Dirichlet matrix factorisation, naive Bayes collaborative filtering and neural collaborative filtering models. Furthermore, the Bernoulli matrix factorisation model also achieves good performance using the normalised discounted cumulative gain quality measure. The integration of the Bernoulli matrix factorisation model with knowledge graphs offers course recommendations that are accurate, diverse and transparent, improving the learning experience.

• Sustainability is defined as a trustworthiness attribute in SW architecture design. • Design of 4 SW architectures for an indoor environmental IoT system in the Edge. • Response time and energy consumption experimental evaluation report of 30 experiments. • 11 findings for the green and performance trustworthy design of IoT SW architectures. • The 30 experiments’ dataset with response and computing times and energy consumption. Identifying and implementing sustainable solutions to address the high energy consumption of ICT solutions has become a necessity in Industry 4.0. Therefore, the construction of Green software products is a priority for its customers, who need to trust that their software products are sustainable to fulfil the sustainability policies of their companies. We present sustainability as a key attribute in addressing the trustworthiness of software architectures. This work is focused on IoT monitoring software architectures in the Edge, since IoT energy consumption is a critical issue for sustainability due to the huge amount of power connections required by IoT devices and the high energy consumption needed for processing and transmitting their data. We present an exploratory research study conducted to determine how to design trustworthy IoT monitoring software architectures in the Edge considering two critical attributes, performance and sustainability. Specifically, this study evaluates how the configuration of the components that comprise Edge IoT monitoring architectures may influence their energy consumption and response time. This work presents the experimental results of the exploratory study and its findings, in which the energy consumption and response time of four different software architecture configurations of an indoor environmental monitoring IoT system are measured. From the execution of thirty experiments, this study reveals the importance of balancing the monitoring activities between the Edge nodes and servers, and it indicates that it is possible to construct trustworthy IoT monitoring software architectures in the Edge with software components that reduce both energy consumption and response time.

Abstract For information and communication technology to reach its goal of zero emissions in 2050, power consumption must be reduced, including the energy consumed by software. To develop sustainability-aware software, green metrics have been implemented to estimate the energy consumed by the execution of an application. However, they have a rebound energy consumption effect because they require an application to be executed to estimate the energy consumed after each change. To address this problem, it is necessary to construct energy estimation models that do not require the execution of applications. This work addresses this problem by constructing a green model based on size, complexity and duplicated lines to estimate the energy consumed by model-view-controller applications without their execution. This article defines a model constructed based on 52 applications. The results were accurate in twelve applications, which showed that the joule estimation was very close to reality, avoiding the energy consumed by the execution of applications.

Nowadays, software systems remain useful and competitive; entail the inevitable need to change over time and to be adapted to new technologies, platforms, and architectures. These quick changes imply following systematic, automated, or standardised processes that provide recommendations and guidelines to architects during software architecture reconstruction. Considerable research work on architecture reconstruction has been conducted. However, it needs to be studied thoroughly to determine what are the common activities and elements to reconstruct software architectures, and to define a reference process for systematically guiding the evolution of software architectures. This work addresses the need for defining a process for software architecture reconstruction called software improvement in the reconstruction of architectures (SIRA). This process has been rigorously designed from the results of a systematic literature review and a small survey of related work. As a result, SIRA integrates and extends previous research and can be conceived as a reference process to reconstruct software architectures in a semi-automated way. In addition, this work also determines the common elements of the architecture reconstruction process: (i) techniques and activities; (ii) architectural elements, patterns, and attributes; (iii) mechanisms and strategies; and (iv) the automation and recommendation tasks of the process.