Abstract
Context: Software testing ensures software quality, but developers often disregard it. The use of automated testing generation is pursued to reduce the consequences of overlooked test cases in a software project. Problem: In the context of Java programs, several tools can completely automate generating unit test sets. Additionally, studies are conducted to offer evidence regarding the quality of the generated test sets. However, it is worth noting that these tools rely on machine learning and other AI algorithms rather than incorporating the latest advancements in Large Language Models (LLMs). Solution: This work aims to evaluate the quality of Java unit tests generated by an OpenAI LLM algorithm, using metrics like code coverage and mutation test score. Method: For this study, 33 programs used by other researchers in the field of automated test generation were selected. This approach was employed to establish a baseline for comparison purposes. For each program, 33 unit test sets were generated automatically, without human interference, by changing Open AI API parameters. After executing each test set, metrics such as code line coverage, mutation score, and success rate of test execution were collected to evaluate the efficiency and effectiveness of each set. Summary of Results: Our findings revealed that the OpenAI LLM test set demonstrated similar performance across all evaluated aspects compared to traditional automated Java test generation tools used in the previous research. These results are particularly remarkable considering the simplicity of the experiment and the fact that the generated test code did not undergo human analysis.

Abstract
The present study aimed to analyze the current state of the art regarding brand hate with the main intention of identifying possible gaps to be explored in future studies. Brand hate can be described as a set of negative emotions on the part of consumers concerning a certain brand, whose implications involve a reduction in the profitability of companies, as well as of their market shares. From the research carried out in the Scopus and Web of Science databases, 90 publications related to the theme were identified, of which 25 were selected and read in full. The analyzed literature points out that research on the subject has focused almost exclusively on the development of the phenomenon and its consequences from the perspective of consumer behavior. Therefore, the emphasis has been on identifying its direct antecedents, on the effects of its mediators in a set of behaviors such as complaints, negative word of mouth, protests, sponsorship reduction and assignment, brand change, and wishes for revenge, among others. Few studies have been dedicated to understanding the direct effects of brand hate on consumer behavior, its evolution over time in different industries and contexts, who its mediators are, and how the phenomenon is perceived and managed from the perspective of the companies involved in this phenomenon, providing opportunities for future research.

Abstract

Abstract
Although city branding is not new, the importance of sustainability and environmental demands is placing an enormous chal-lenge in city/place branding activities. As such, the aim of this article is to analyze how sustainable/green city/place branding is understood and what its main idiosyncrasies are. For that an exploratory literature review was implemented and 32 articles were analyzed. It is possible to conclude that there are three main strands covering the topic related to green, sustainable, and slow city (cittaslow) or place branding. Moreover, green resources are dealt with seeking to improve the image of the city, the quality of urban life, and the green spaces supporting the city as a tourism destination. Complementarily, sustainabil-ity embraces also economic and social aspects, which are not fully covered in the previous strand. Finally, the cittaslow perspective follows a sustainable perspective more closely than the green/environmental one. The three strands are very segmented and the stage of development is still in a growing up stage. The stakeholders play an important role in disclosing the natural resources, the environmental challenges for a city/place to develop a positive sustainable reputation. Finally, there is an important role from the public policy perspective to position the city as a green, sustainable place destination.

Abstract
In this article the influence of the maximum partition size on the performance of a discrete version of the Bat Algorithm (BA) is studied. The Bat Algorithm is a population-based meta-heuristic based on swarm intelligence developed for continuous problems with exceptional results. Thus, it has a set of parameters that must be studied in order to enhance the performance of the meta-heuristic. This paper aims to investigate whether the maximum size of the partitions used for the search operations throughout the algorithm should not also be considered as a parameter. First, a literature review was conducted, with special focus on the parameterization of the meta-heuristics and each of the parameters currently used in the algorithm, followed by its implementation in VBA in Microsoft Excel. After a thorough parameterization of the discrete algorithm, different maximum partition sizes were applied to 30 normally distributed instances to draw broader conclusions. In addition, they were also tested for different sizes of the problem to see if they had an influence on the results obtained. Finally, a statistical analysis was carried out, where it was possible to conclude that there was no maximum partition value for which superiority could be proven, and so the size of the partition should be considered a parameter in the bat algorithm and included in the parametrization of BA. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
Chest radiography is one of the most ubiquitous medical imaging modalities. Nevertheless, the interpretation of chest radiography images is time-consuming, complex and subject to observer variability. As such, automated diagnosis systems for pathology detection have been proposed, aiming to reduce the burden on radiologists. The advent of deep learning has fostered the development of solutions for both abnormality detection with promising results. However, these tools suffer from poor explainability as the reasons that led to a decision cannot be easily understood, representing a major hurdle for their adoption in clinical practice. In order to overcome this issue, a method for chest radiography abnormality detection is presented which relies on an object detection framework to detect individual findings and thus separate normal and abnormal CXRs. It is shown that this framework is capable of an excellent performance in abnormality detection (AUC: 0.993), outperforming other state-of-the-art classification methodologies (AUC: 0.976 using the same classes). Furthermore, validation on external datasets shows that the proposed framework has a smaller drop in performance when applied to previously unseen data (21.9% vs 23.4% on average). Several approaches for object detection are compared and it is shown that merging pathology classes to minimize radiologist variability improves the localization of abnormal regions (0.529 vs 0.491 APF when using all pathology classes), resulting in a network which is more explainable and thus more suitable for integration in clinical practice.