This article examines digital transformation and technology adoption within small and medium-sized enterprises (SMEs) in developing countries. It focuses on identifying the mechanisms that facilitate or hinder the integration of new technologies in these contexts, often constrained by specific socioeconomic and cultural factors. The research employed a systematic literature review, adhering to a structured protocol to explore existing studies. Critical stages of this review included defining research questions, developing a comprehensive search strategy, and systematically selecting and analyzing relevant studies from significant databases. The review reveals a significant emphasis on basic information systems as the primary form of technology adoption in SMEs within developing countries, with more advanced technologies like cloud computing and artificial intelligence being less common. Factors such as the entrepreneurial ecosystem and external social influences, including government and private sector initiatives, play pivotal roles in shaping these adoption patterns. This study underscores the critical role of community-driven efforts in promoting technology adoption over initiatives by the public and private sectors. The findings suggest a need for future research to develop tailored technological adoption models that accommodate the unique challenges of SMEs in developing countries. Additionally, more inclusive approaches that consider sociocultural dynamics could further enhance the effectiveness of technology integration strategies in these regions.

Software development must be based on more than just the experience and capabilities of your programmers and your team. The importance of obtaining a quality product lies in the risks that can be exploited by software vulnerabilities, which can jeopardize organizational assets, consumer confidence, operations, and a broad spectrum of applications. Several methods, techniques, and models have been suggested and developed to address software security. However, only a few have solid evidence for creating secure software applications. The main objective of this paper is to survey the literature for methods or models suitable for considering the integration of security in all or some of the phases of the software development life cycle and which ones are most considered or neglected. This study represents the beginning of research to generate a methodology that integrates security practices in agile software development, allowing inexperienced developers to create more secure applications.

Children with autism spectrum disorder (ASD) have deficits in social interaction and expressing and understanding emotions. Based on this, robots for children with ASD have been proposed. However, few studies have been conducted about how to design a social robot for children with ASD. Non-experimental studies have been carried out to evaluate social robots; however, the general methodology that should be used to design a social robot is not clear. This study proposes a design path for a social robot for emotional communication for children with ASD following a user-centered design approach. This design path was applied to a case study and evaluated by a group of experts in psychology, human–robot interaction, and human–computer interaction from Chile and Colombia, as well as parents of children with ASD. Our results show that following the proposed design path for a social robot to communicate emotions for children with ASD is favorable.

The massive use of internet provides information access all over the world. It represents a potential benefit for everyone, but also brings disadvantages when it comes to deliver the information in an effective manner. Intercultural aspects have to be explicitly considered when developing web applications. This paper proposes a set of usability heuristics that includes cultural aspects. Preliminary validation proves its potential when evaluating web sites.

While some areas of software engineering knowledge present great advances with respect to the automation of processes, tools, and practices, areas such as software maintenance have scarcely been addressed by either industry or academia, thus delegating the solution of technical tasks or human capital to manual or semiautomatic forms. In this context, machine learning (ML) techniques play an important role when it comes to improving maintenance processes and automation practices that can accelerate delegated but highly critical stages when the software launches. The aim of this article is to gain a global understanding of the state of ML-based software maintenance by using the compilation, classification, and analysis of a set of studies related to the topic. The study was conducted by applying a systematic mapping study protocol, which was characterized by the use of a set of stages that strengthen its replicability. The review identified a total of 3776 research articles that were subjected to four filtering stages, ultimately selecting 81 articles that were analyzed thematically. The results reveal an abundance of proposals that use neural networks applied to preventive maintenance and case studies that incorporate ML in subjects of maintenance management and management of the people who carry out these tasks. In the same way, a significant number of studies lack the minimum characteristics of replicability.