Abstract
The theme of this Special Issue (SI) is Sustainable Marketing and Strategy, as in the literature, we have seen growing evidence of how sustainability efforts are increasingly bringing significant benefits to enterprises [...]

Abstract
This chapter describes a software architecture for processing big-data analytics considering the complete compute continuum, from the edge to the cloud. The new generation of smart systems requires processing a vast amount of diverse information from distributed data sources. The software architecture presented in this chapter addresses two main challenges. On the one hand, a new elasticity concept enables smart systems to satisfy the performance requirements of extreme-scale analytics workloads. By extending the elasticity concept (known at cloud side) across the compute continuum in a fog computing environment, combined with the usage of advanced heterogeneous hardware architectures at the edge side, the capabilities of the extreme-scale analytics can significantly increase, integrating both responsive data-in-motion and latent data-at-rest analytics into a single solution. On the other hand, the software architecture also focuses on the fulfilment of the non-functional properties inherited from smart systems, such as real-time, energy-efficiency, communication quality and security, that are of paramount importance for many application domains such as smart cities, smart mobility and smart manufacturing. © The Author(s) 2022. All rights reserved.

Abstract
Practical programming competencies are critical to the success in computer science (CS) education and goto-market of fresh graduates. Acquiring the required level of skills is a long journey of discovery, trial and error, and optimization seeking through a broad range of programming activities that learners must perform themselves. It is not reasonable to consider that teachers could evaluate all attempts that the average learner should develop multiplied by the number of students enrolled in a course, much less in a timely, deep, and fair fashion. Unsurprisingly, exploring the formal structure of programs to automate the assessment of certain features has long been a hot topic among CS education practitioners. Assessing a program is considerably more complex than asserting its functional correctness, as the proliferation of tools and techniques in the literature over the past decades indicates. Program efficiency, behavior, and readability, among many other features, assessed either statically or dynamically, are now also relevant for automatic evaluation. The outcome of an evaluation evolved from the primordial Boolean values to information about errors and tips on how to advance, possibly taking into account similar solutions. This work surveys the state of the art in the automated assessment of CS assignments, focusing on the supported types of exercises, security measures adopted, testing techniques used, type of feedback produced, and the information they offer the teacher to understand and optimize learning. A new era of automated assessment, capitalizing on static analysis techniques and containerization, has been identified. Furthermore, this review presents several other findings from the conducted review, discusses the current challenges of the field, and proposes some future research directions.

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Abstract
Testing software is very important, but not done well, resulting in problematic and erroneous software applications. The cause radicates from a skills mismatch between what is needed in industry, the learning needs of students, and the way testing is currently being taught at higher and vocational education institutes. The goal of this project is to identify and design seamless teaching materials for testing that are aligned with industry and learning needs. To represent the entire socio-economic environment that will benefit from the results, this project consortium is composed of a diverse set of partners ranging from universities to small enterprises. The project starts with research in sensemaking and cognitive models when doing and learning testing. Moreover, a study will be done to identify the needs of industry for training and knowledge transfer processes for testing. Based on the outcomes of this research and the study, we will design and develop capsules on teaching software testing including the instructional materials that take into account the cognitive models of students and the industry needs. Finally, we will validate these teaching testing capsules developed during the project.

Abstract
Building renewable energy communities (REC) involves investments on generation facilities (such as PV panels), technologies to provide flexibility (such as batteries), management platforms and ICT systems, as well as integrating other flexibility sources such as thermal storage or electric vehicles. The way investments are made by the REC's members and other third parties is in close relationship with the governance models of the REC in terms of energy, flexibility and costs and benefits sharing, which, in the end, constitute the overall REC's business model. This works provides a revision of the main financing mechanisms to invest on and build a REC, and of the associated governance and business models that result from the investments mechanisms selected and its implications on its day by day operation. © 2022 IEEE.