Félix García

Background: Software plays a critical role in modern society, but its energy consumption is a growing concern. This issue is especially evident in the Internet of Things (IoT), which involves billions of devices operating under strict energy constraints, most of which rely on C Programming Language (PL) because of its energy efficiency. Outside of IoT, PLs like Python and Java have gained popularity despite being less energy‐efficient, particularly Python. Optimization is therefore essential to reduce energy use in both contexts: (i) in the IoT, where the large number of devices causes high cumulative energy consumption despite using C; and (ii) for Python and Java, given their widespread adoption and higher energy demands. However, software optimizations do not always lead to lower energy consumption. Therefore, their impact on energy efficiency must be evaluated carefully. Method: This article evaluates the energy impact of 26 optimization techniques in C, Python, and Java across different compiler and runtime configurations. The main focus is on comparing baseline and optimized implementations under each configuration. In addition, the study investigates the effect of compiler and runtime settings—such as ‐O0 versus ‐O3 in C, CPython versus Nuitka in Python, and interpreter mode versus Just‐In‐Time (JIT) compilation in Java—applied consistently to both baseline and optimized versions to assess their influence on energy consumption. Results: The results show that Python achieved the best overall improvement, with 58% of the optimization techniques resulting in energy savings, achieving reductions of up to 70.38%. However, despite this high effectiveness in reducing energy consumption, Python still exhibits the highest overall energy consumption among the three PLs.

Sustainability in software and green software has increased attention in software engineering (SE), given the potential of software to contribute positively to sustainable development initiatives. This trend is substantiated by the numerous systematic reviews (SRs) that have been published on the matter. However, a significant gap in understanding remains concerning the sustainability concepts and their dimensions within the field. This tertiary study aims to construct a comprehensive map of sustainability topics in SE. As a result, 80 SRs were selected in this report. A notable lack of consensus exists regarding SE sustainability definitions and the dimensions involved. The environmental dimension is the most frequently reported, capturing 55% of the research focus, with a predominant emphasis on energy efficiency. Approximately 52.5% of studies delve into products related to system performance and evolvability and software architecture. However, limited attention is given to the social, economic, and individual dimensions. Despite the considerable focus on the green dimension, there is a necessity for further research in both empirical primary studies and SRs to enhance the reliability of the findings. Achieving consensus on definitions of terminology and implementing standardized measurement methods are essential steps to advance understanding the sustainable SE.

1. The business process architecture of an enterprise refers to the organisation of its collection of business processes (Green and Ould 2005). As a component of the business architecture domain of...

In the wake of the COVID-19 pandemic, remote working seemed here to stay. However, attempts to return to working in the office are currently being made, mainly by large multinationals. We therefore set out to investigate software-industry workers’ opinions of their productivity in their current work mode. This paper presents the results of surveys applied to software engineers from Mexico, Spain, Colombia and Argentina with the purpose of analysing how they perceive their productivity and that of their team when working either partially or totally in a remote manner. The results indicate that most of the respondents prefer to work remotely. Furthermore, according to their perception, their productivity and that of their team is, in general, equal or superior to that achieved in the office.

Researchers, organizations, and professionals have a great interest in defining sustainability indicators within the framework of Green Business Process Management (Green BPM), together with business process models that allow the impact of those indicators to be assessed in organizations. However, there is a lack of procedures that help organizations to incorporate indicators in Green BPM. For this reason, the so‐called Gaia method has been developed based on the Green BPM lifecycle. This method allows the implementation, monitoring, and continuous improvement of sustainability in business processes based on sustainability indicators. This paper presents an empirical validation of Gaia through a case study carried out in a higher education institution, emphasizing the definition and evaluation of sustainability indicators in business processes. The evaluation of sustainability indicators into the BPM lifecycle allowed sustainability indices to be obtained for each process, leading to the calculation of an organization's sustainability coefficient. The proposal has been applied to a higher education organization, which obtained a sustainability coefficient , corresponding to the medium sustainability level (). The case study has been the basis for the re‐elaboration of objectives, lines of action, and the redesign of business processes that contribute to evaluating and improving the levels of sustainability desired by the organization under study. The main limitation of our study is that the Gaia method has been applied on one organization only. A tool that automates the method to assess the sustainability of business processes is being developed.

Quantum computing has emerged as a promising field with the potential to revolutionize information processing and storage. Understanding its current state and research trajectory is crucial. This study aims to map the current literature on quantum computing, to identify how quantum computing is being used in solving quantum software problems and improving quantum algorithms. Moreover, it examines evaluation parameters and the challenges faced in this field. A systematic mapping study was conducted using the SCOPUS database, including all papers up to December 2024. Relevant articles were selected based on inclusion criteria, and a content analysis was performed to categorize and synthesize the findings. Information was gathered from 33 primary studies. Findings show that quantum computing is mainly applied in machine learning and optimization, though classical methods still outperform it. IBM Quantum is the most used platform. Research focuses on algorithm efficiency, verification over validation, and resource optimization, but lacks standardized evaluation methods. Key limitations include hardware constraints, noise, and scalability, while challenges involve integration, error correction, and problem formulation. Quantum computing continues to face significant technological challenges, but current research lays the groundwork for future advances. More practical studies on real hardware and strategies to improve the implementation and performance of quantum algorithms are needed. Effective evaluation, verification, and validation of quantum solutions must be further investigated.