The old electricity network infrastructure has proven to be inadequate, with respect to modern challenges such as alternative energy sources, electricity demand and energy saving policies. Moreover, Information and Communication Technologies (ICT) seem to have reached an adequate level of reliability and flexibility in order to support a new concept of electricity network — the smart grid. In this work, we will analyse the state-of-the-art of smart grids, in their technical, management, security, and optimization aspects. We will also provide a brief overview of the regulatory aspects involved in the development of a smart grid, mainly from the viewpoint of the European Union.

Large Language Models (LLM) have rapidly affirmed in the latest years as a means to support or substitute human actors in a variety of tasks. LLM agents can generate valid software models, because of their inherent ability in evaluating textual requirements provided to them in the form of prompts. The goal of this work is to evaluate the capability of LLM agents to correctly generate UML class diagrams in activities of Requirements Modeling in the field of Software Engineering. Our aim is to evaluate LLMs in an educational setting, i.e., understanding how valuable are the results of LLMs when compared to results made by human actors, and how valuable can LLM be to generate sample solutions to provide to students. For that purpose, we collected 20 exercises from a diverse set of web sources and compared the models generated by a human and an LLM solver in terms of syntactic, semantic, pragmatic correctness, and distance from a provided reference solution. Our results show that the solutions generated by an LLM solver typically present a significantly higher number of errors in terms of semantic quality and textual difference against the provided reference solution, while no significant difference is found in syntactic and pragmatic quality. We can therefore conclude that, with a limited amount of errors mostly related to the textual content of the solution, UML diagrams generated by LLM agents have the same level of understandability as those generated by humans, and exhibit the same frequency in violating rules of UML Class Diagrams.

Gamification is an established practice in Software Engineering to increase effectiveness and engagement in many practices. This manuscript provides a characterization of the application of gamification to the Software Testing area. Such practice in fact reportedly suffers from low engagement by both personnel in industrial contexts and learners in educational contexts. Our goal is to identify the application areas and utilized gamified techniques and mechanics, the provided benefits and drawbacks, as well as the open challenges in the field. To this purpose, we conducted a Multivocal Literature Review to identify white and grey literature sources addressing gamified software testing. We analyzed 73 contributions and summarized the most common gamified mechanics, concepts, tools, and domains where they are mostly applied. We conclude that gamification in software testing is mostly applied to the test creation phase with simple white-box unit or mutation testing tools and is mostly used to foster good behaviors by promoting the testers’ accomplishment. Key research areas and main challenges in the field are: careful design of tailored gamified mechanics for specific testing techniques; the need for technological improvements to enable crowdsourcing, cooperation, and concurrency; the necessity for empirical and large-scale evaluation of the benefits delivered by gamification mechanics.

The energy efficiency of IT has become one of the hottest topics in the last few years. The problem has been typically addressed by hardware manufacturers and designers, but recently the attention of industry and academia has shifted to the role of software for IT sustainability. Writing energy-efficient software is one of the most challenging issues in this area, because it requires not only a change of mindset for software developers and designers but also models and tools to measure and reduce the effect of software on the energy consumption of the underlying hardware. In this article, the authors present a conceptual framework that provides a unifying view of the strategies, models, and tools available so far for designing and developing greener software.

Software maintainability is a crucial property of software projects. It can be defined as the ease with which a software system or component can be modified to be corrected, improved, or adapted to its environment. The software engineering literature proposes many models and metrics to predict the maintainability of a software project statically. However, there is no common accordance with the most dependable metrics or metric suites to evaluate such nonfunctional property. The goals of the present manuscript are as follows: (i) providing an overview of the most popular maintainability metrics according to the related literature; (ii) finding what tools are available to evaluate software maintainability; and (iii) linking the most popular metrics with the available tools and the most common programming languages. To this end, we performed a systematic literature review, following Kitchenham’s SLR guidelines, on the most relevant scientific digital libraries. The SLR outcome provided us with 174 software metrics, among which we identified a set of 15 most commonly mentioned ones, and 19 metric computation tools available to practitioners. We found optimal sets of at most five tools to cover all the most commonly mentioned metrics. The results also highlight missing tool coverage for some metrics on commonly used programming languages and minimal coverage of metrics for newer or less popular programming languages. We consider these results valuable for researchers and practitioners who want to find the best selection of tools to evaluate the maintainability of their projects or to bridge the discussed coverage gaps for newer programming languages.