• WEs with high similarity guided developers to relevant directories and files. • Eye-tracking revealed the benefits and challenges of using WEs in OSS projects. • Data beyond the title and description is needed to obtain more relevant WEs. • WEs served as a starting point for resolving issues on GitHub. • WEs complement LLMs by offering community-validated solutions. The growing popularity of Open-Source Software projects has raised questions about the challenges novice and inexperienced developers face, especially on code contribution platforms like GitHub. This study investigates the effects of using Worked Examples (WEs) to support these developers in solving coding tasks, using eye-tracking and cognitive effort analysis. The research involved 20 undergraduate students analyzing issues from the JabRef repository, with recommendations of high and low-similarity examples provided by a bot. The findings suggest that highly similar WEs effectively guided participants by helping identify relevant directories, files, and code snippets, serving as starting points for task resolution. However, challenges emerged, such as difficulties locating useful information and risks of false proximity between seemingly similar issues. These results highlight the need for improved recommendation strategies beyond textual similarity, incorporating structural elements such as file and method names, while reducing cognitive load through better presentation of relevant information. This work lays the groundwork for exploring WEs in Open-Source Software projects and opens avenues for further research, including validating findings in other repositories and understanding behavioral patterns in using WEs.

Abstract The demand for soybean tends to increase worldwide with population growth. Despite farmers adopting strategies such as crop rotation, soil preparation, and the application of chemical products, the most critical factor to increase productivity is choosing cultivars adapted to the region so that they are resistant to deceases. In addition, seed quality also directly influences crop productivity. To help a farmer select suitable cultivars and analyze the quality of soybean seeds, we present an application that uses the user’s location to identify suitable cultivars based on the edaphoclimatic characteristics of a region. The details of these cultivars are shown in text, images, and video, making it possible to compare the selected varieties. Our app also contains a trained convolution neural network capable of classifying the quality of soybean seeds based on an image captured by the user or stored on a smartphone. The convolutional neural network architecture allowed an excellent performance, with an accuracy of 94.06% in the classification of soybean seeds. All app functionality runs comfortably on mobile devices. Compared to others that have the same purpose, our application has a more significant number of features.

Smart cities aim to address urban challenges such as traffic congestion, pollution, and limited resources. Software platforms play a key role in integrating diverse technologies and services, enabling real-time data processing to support efficient public service delivery. Cloud computing meets the demands of critical applications—like autonomous vehicles and high-load systems—by offering scalable infrastructure with dynamic resource provisioning. However, the lack of structured guidelines for migrating Web applications to the cloud, especially in smart city environments, remains a significant challenge. Migration involves technical adaptation and strategic decisions regarding resource allocation and service configuration. This paper presents a goal-oriented catalogue based on the KAOS (Keep All Objectives Satisfied) model to support web application migration to the cloud in smart city contexts. The catalogue defines goals related to five essential cloud services: relational databases, virtual machines, blob storage, application deployment, and queuing services. It serves as a decision-support tool for developers and urban planners. The proposed approach will be integrated into a smart city platform and validated through case studies and simulations.

Microservices is a modern architectural style in which developers decomposes a software system into many services loose coupled with small responsibilities. Given its inherent complexity, many frameworks have been proposed in order to support developers in microservices. However, due to its particularities, the whole process of choosing the most appropriate framework for developers' needs is a time-consuming and challenging task. In this paper, we present a qualitative study that compares both KumuluzEE and Spring Cloud & NetFlix OSS frameworks through functional and non-functional requirements. We tested each framework by developing a hypothetical scenario with each of them. Our results show that although the KumuluzEE supports few characteristics of the microservices architecture, it is easier to use, especially, for newcomers. Instead, the Spring Cloud & NetFlix OSS is suitable for large-scale systems and experienced development teams, and it holds a higher number of the architecture characteristics. However, learnability for newcomers is low even though the framework provides a substantial documentation.

Predicting defects in software projects is a complex task, especially in the initial phases of software development because there are a few available data. The use of cross-project defect prediction is indicated in such situation because it enables to reuse data of similar projects. In order to find and group similar projects, this paper proposes the construction of cross-project prediction models using a measure of performance achieved through the application of classification algorithms. To do so, we studied the combined application of different algorithms of classification, of feature selection, and clustering data, applied to 1270 projects aiming to building different cross-project prediction models. In this study we concluded that Naive Bayes algorithm obtained the best performance, with 31.58 % of satisfactory predictions in 19 models created with its use. This proposal seems to be promise, once the local predictions considered satisfactory reached 31.58%, against 26.31 % of global predictions.

Multi-cloud APIs, such as jclouds, have been regarded as central players in achieving cloud portability and managing multiple clouds. Although their benefits, little is known about their performance. This is critical because applications can suffer performance degradation if the overhead created by a multi-cloud API is significantly larger than a platform specific API. Furthermore, if multi-cloud APIs prove not to be cost-effective, it can influence the selection of a solution for cloud portability. By carrying out two quasi-experiments, we identified that the performance of jclouds varies according to the cloud platform it targets. This finding contributes to the cloud community by showing a possible trade-off of multi-cloud APIs and providing a quantitative criterion to be analysed when adopting multiple cloud solutions.