The rapid evolution of blockchain technology has created a diverse ecosystem of platforms, tools, and applications, and understanding how these technologies are adopted and used in practice is essential for advancing Blockchain-Oriented Software Engineering. This paper presents Web3BlockSet, a curated dataset that enables empirical insights into the blockchain ecosystem through the analysis of Mining Software Repositories on GitHub. We collected 391,596 Issues and Pull Requests from organizations that maintain core blockchain technologies and from community developers who create applications. The dataset offers broad categorization across the blockchain stack, a dual perspective of technology vendors and adopters, and rich metadata that supports diverse analytical approaches.

Blockchain has been relevant in the document management process, serving as a storage solution with the potential to guarantee the relevant requirements needed for any document storage and validation solution. However, due to the distributed nature of blockchain, we may face implementation difficulties and high operational costs, for example. To facilitate this process, we propose a customizable blockchain-based document registration service that makes it possible to create different types of gen-eralized documents for various application domains and store them in one or more blockchains integrated in an Application Programming Interface (API).

Overweight and obesity in children is a recognized worldwide epidemic. They are associated with several current and future chronic diseases. OCARIoT is a joint EU-Brazil joint that aims to develop a sophisticated, noninvasive, unobtrusive, personalized IoT system to detect and normalize the behaviors that put a child at risk of developing obesity or eating disorders. In a recent written work, we proposed the design of an agent-based approach to recommend individual physical and food-related activities, based on data collected from wearable devices. In this paper, we present the design of an expanded approach that, in addition to recommendations for individual activities, should recommend activity plans, i.e., sequences of activities organized to minimize childhood obesity. The first results with the extended version were very promising. During the experiments, the selected individual activities and sequences of activities organized by the approach proved to be effective in conducting children, with different profiles and initial states, to the desired states of various attributes associated with childhood obesity.

Obesity is one of the most significant public health problems of the 21st century, having been recognized by the World Health Organization as the epidemic of this century. This problem has been affecting men, women, and children of all races and all ages, particularly in urban areas. OCARIoT is a research and development project financed by the Rede Nacional de Pesquisa (Brazil) and the European Union to conceive a technological solution based on the Internet of Things to face Childhood Obesity. An OCARIoT solution is to use a Decision Support System to encourage children to have healthy habits, with the help of IoT devices. This work presents the development of an algorithm for detecting unhealthy trends and forecast values in time series, which will be a tool to assist the Decision Support System.

Search Based Software Engineering (SBSE) seeks to reformulate Software Engineering complex problems as search problems to be, hereafter, optimized through the usage of artificial intelligence techniques. As pointed out by Harman in 2007, in his seminal paper about the current state and future of SBSE, it would be very attractive to have convincing examples of human competitive results in order to champion the field. A landmark effort in this direction was made by Souza and others, in the paper titled “The Human Competitiveness of Search Based Software Engineering”, published at SSBSE’2010, voted by the SBSE community as the most influential paper of the past editions in the 10th anniversary of the SSBSE, in 2018. This paper presents a preliminary systematic mapping study to provide an overview of the current state of human competitiveness of SBSE, carried out via a snowball reading of Souza’s paper. The analyses of the 29 selected papers showed a growing interest in this topic, especially since 2010. Seven of those papers presented relevant experimental results, thus demonstrating the human competitiveness of results produced by SBSE approaches.

Search Based Software Engineering approaches have proven to be feasible and promising in tackling a number of software engineering problems. More recently, researchers have been considering the challenges and opportunities related to involving users’ expertise in the resolution process, among other reasons, to deal with the mistrust or misunderstanding of fully automated optimisation approaches. This paper presents a preliminary study concerned at assessing the users’ subjective perception when his/her preferences are considered in an Interactive SBSE approach. Regarding the evaluation, we conducted a placebo-controlled study with 12 software engineering practitioners by simulating a Next Release Problem scenario. The results indicate that most (68%) of the gain achieved by the interactive approach could be attributed to being the placebo effect, that is, refers strictly to the fact that the user felt part of the optimisation process. In addition, there was an important increased confidence in the results, even in the placebo group.

The next release problem is a significant task in the iterative and incremental software development model, involving the selection of a set of requirements to be included in the next software release. Given the dynamic environment in which modern software development occurs, the uncertainties related to the input variables considered in this problem should be taken into account. In this context, this paper proposes a novel formulation to the next release problem based on scenarios and considering the robust optimization framework, which enables the production of robust solutions. In order to measure the "price of robustness," several experiments were designed and executed over artificial and real-world instances. All experimental results are consistent to show that the penalization with regard to solution quality due to robustness is relatively small, which qualifies the proposed model to be applied even in large-scale real-world software projects.

This paper proposes a new approach to data selection, a key issue in classification problems. This approach, which is based on a feature selection algorithm and one instance selection algorithm, reduces the original dataset in two dimensions, selecting relevant features and retaining important instances simultaneously. The search processes for the best feature and instance subsets occur separately yet, due to the influence of features in the importance of instances and vice versa, they bias one another. The experiments validate the proposed approach showing that this existing relation between features and instances can be reproduced when constructing data selection algorithms and that it leads to a quality improval comparing to the sequential execution of both algorithms.