The new era of information and the needs of our society require continuous change in software and technology. Changes are produced very quickly and software systems require evolving at the same velocity, which implies that the decision-making process of software architectures should be (semi-)automated to satisfy changing needs and to avoid wrong decisions. This issue is critical since suboptimal architecture design decisions may lead to high cost and poor software quality. Therefore, systematic and (semi-)automated mechanisms that help software architects during the decision-making process are required. Architectural patterns are one of the most important features of software applications, but the same pattern can be implemented in different ways, leaving to results of different quality. When an application requires to evolve, knowledge extracted from similar applications is useful for driving decisions, since quality pattern implementations can be reproduced in similar applications to improve specific quality attributes. Therefore, clustering methods are especially suitable for classifying similar pattern implementations. In this paper, we apply a novel unsupervised clustering technique, based on the well-known artificial neural network model Self-Organizing Maps, to classify Model-View-Controller (MVC) pattern from a quality point of view. Software quality is analyzed by 24 metrics organized into the categories of Count/Size, Maintainability, Duplications, Complexity, and Design Quality. The main goal of this work is twofold: to identify the quality features that establish the similarity of MVC applications without software architect bias, and to classify MVC applications by means of Self-Organizing Maps based on quality metrics. To that end, this work performs an exploratory study by conducting two analyses with a dataset of 87 Java MVC applications characterized by the 24 metrics and two attributes that describe the technology dimension of the application. The stated findings provide a knowledge base that can help in the decision-making process for the architecture of Java MVC applications.

This work aims to evaluate the use of Learning Analitycs (LA) in Higher Education; students, and attributes such as: profile, interactions in a virtual learning environment learning - for this use business intelligence paradigm in order to explore and exploit the data from one of the actors in the educational process is analyzed, test scores, among others, which will contribute to their educational success. In particular, this paper tries to answer the following specific objectives: To identify factors that influence the decision of a college student distance learning to abandon their studies and get the profile of potentially susceptible students from their university studies. To meet this purpose we define two analysis tasks learning and use a business intelligence methodology to implement it.

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.

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.

This work addresses the problem of student dropout in the university environment, where students abandon their studies at any phase of their career, for reasons ranging from personal and economic factors to academic dissatisfaction. This results in the student not starting the degree, or dropping out in early cycles and some cases in the final cycles, affecting the continuity of the study program, and at the same time the students do not reach their profession. With the help of Artificial Intelligence, it is possible to find patterns that allow us to avoid these scenarios early, providing universities with useful information to establish mitigation strategies. This research proposes a prediction model for student dropout in critical subjects, using classic Machine Learning techniques and advanced Deep Learning algorithms, integrated under the KDD method, for analysis of the academic information of the Computer Science degree over 2 years. The results of the model allow us to identify the techniques and algorithms with greater precision and at the same time recognize patterns that establish the dropout trend in core and pre-professional subjects.