The manual generation of test scripts is a time-intensive, costly, and error-prone process, indicating the value of automated solutions. Large Language Models (LLMs) have shown great promise in this domain, leveraging their extensive knowledge to produce test code more efficiently. This study investigates the quality of Python test code generated by three LLMs: GPT-4o, Amazon Q, and LLama 3.3. We evaluate the structural reliability of test suites generated under two distinct prompt contexts: Text2Code (T2C) and Code2Code (C2C). Our analysis includes the identification of errors and test smells, with a focus on correlating these issues to inadequate design patterns. Our findings reveal that most test suites generated by the LLMs contained at least one error or test smell. Assertion errors were the most common, comprising 64% of all identified errors, while the test smell Lack of Cohesion of Test Cases was the most frequently detected (41%). Prompt context significantly influenced test quality; textual prompts with detailed instructions often yielded tests with fewer errors but a higher incidence of test smells. Among the evaluated LLMs, GPT-4o produced the fewest errors in both contexts (10% in C2C and 6% in T2C), whereas Amazon Q had the highest error rates (19% in C2C and 28% in T2C). For test smells, Amazon Q had fewer detections in the C2C context (9%), while LLama 3.3 performed best in the T2C context (10%). Additionally, we observed a strong relationship between specific errors, such as assertion or indentation issues, and test case cohesion smells. These findings demonstrate opportunities for improving the quality of test generation by LLMs and highlight the need for future research to explore optimized generation scenarios and better prompt engineering strategies.

Indoor positioning is an important element in context-aware applications, especially based on the internet of things (IoT). Elderly people or people with specific needs can benefit from such applications by providing them with an ambient assisted living (AAL). Indoor positioning system (IPS) applications can be deployed under an IoT infrastructure to provide location awareness to other applications, such as AAL solutions. However, software patterns and architectures in the context of IoT applications are still open research topics. This paper proposes a software architecture for IoT-based IPS applications to support AAL systems. We performed a systematic literature review (SLR) to identify solutions and non-functional requirements (NFRs) to incorporate into the architecture of these applications. From the SLR results, we developed an architecture for IoT-based IPS applications, using Fog Computing elements, to support AAL systems. We evaluate the architecture using the iFogSim simulation environment regarding latency, network usage, and energy consumption. The proposed approach was compared to a cloud-based deployment. Experimental results show that the proposed fog-based architecture significantly reduces latency, network usage, and operational costs and is suitable for real-time response scenarios. In addition, the energy consumption of cloud data centers is reduced by employing fog computing.

Background: Several researchers report the impact of gender on software development teams, especially in relation to women. In general, women are under-represented on these teams and face challenges and difficulties in their workplaces. When it comes to women who are mothers, these challenges can be amplified and directly impact these women’s professional lives, both in industry and academia. However, little is known about women ICT practitioners’ perceptions of the challenges of maternity in their professional careers. Objective: This paper investigates mothers’ challenges and difficulties in global software development teams. Method: We conducted a survey with women in the ICT field who work in academia and global technology companies. We surveyed 141 mothers from different countries and employed mixed methods to analyze the data. Results: Our findings reveal that women face sociocultural challenges, including work-life balance issues, bad jokes, and moral harassment. The prejudices they suffer make them insecure and with low confidence in the work performed. Furthermore, they usually do not have a supporting network during and after maternity leave, which culminates in them feeling overloaded. The surveyed women suggested a set of actions to reduce the challenges they face in their workplaces, such as: creating a code of conduct for men and childcare within companies. Conclusion: Women face many challenges when they become mothers. Our findings explore these challenges and can help organizations in developing policies to minimize them. Also, it can help raise awareness of co-workers and bosses, toward a more friendly and inclusive workplace.

E-health can be defined as a set of health-related computing solutions that uses the Internet to provide services. In these systems, managing the sharing of the various types of generated data is a challenge, making it difficult to develop and maintain. A software intensive system is a system in which software essentially influences the design, construction, deployment, and evolution of the system as a whole to encompass individual applications, subsystems, systems of systems, and aggregations of interest. Blockchain has emerged as a solution to provide privacy and data security, providing interesting solutions for E-health applications. The objective of this work is to present architectural and behavioral aspects of E-health systems, using Blockchain. In addition, some research opportunities are also presented.

With the increase in the volume of computer networks usage, effective management is becoming increasingly difficult. Software Defined Networks (SDN) have emerged to address this situation. The objective of this work is to demonstrate the benefits of using SDN control policies through load balancing strategies in accessing servers. For this, we developed multiple access tests on servers submitted directly to the control access provided by the manager, demonstrating how different algorithms can affect the quality of bandwidth and latency in a structure with limited servers. The algorithms evaluated were Round-Robin, Random and Least-Bandwidth. The results indicated that Round-Robin behaved better than the other access scales in environments with limited servers, and the algorithm that used the least trafficked link showed better scalability when managing multiple servers and requisitions.

Due to the growth of urbanization, cities have faced social, economic and environmental transformations. In addition, many vehicles currently have several sensors and actuators, capable of performing not only the sensing of the condition of vehicles, but also the environment around them, and this data can be used for various services. The environment of a large university may resemble urban environments, considering that these institutions compare to cities in various aspects, especially in relation to infrastructure problems. The objective of this work is to develop a solution for the monitoring and prediction of bus stops in university transportation. Tests were performed with six online and offline machine learning algorithms in order to analyze which algorithm is most efficient based on the fixed metrics. The best algorithm presented an absolute prediction error of 20 seconds, which shows the quality of the generated final model.

Software design is an important concern in modern code review through which multiple developers actively discuss and improve each single code change. However, there is little understanding of the impact of such developers' reviews on continuously reducing design degradation over time. It is even less clear to what extent and how design degradation is reversed during the process of each single code change's review. In summary, existing studies have not assessed how the process of design degradation evolution is impacted along: (i) within each single review, and (ii) across multiple reviews. As a consequence, one cannot understand how certain code review practices consistently contribute to either reduce or further increase design degradation as the project evolves. We aim at addressing these gaps through a multi-project retrospective study. By investigating 14,971 code reviews from seven software projects, we report the first study that characterizes how the process of design degradation evolves within each review and across multiple reviews. Moreover, we analyze a comprehensive suite of metrics to enable us to observe the influence of certain code review practices on combating or even accelerating design degradation. Our results show that the majority of code reviews had little to no design degradation impact in the analyzed projects. Even worse, this observation also applies, to some extent, to reviews with an explicit concern on design. Surprisingly, the practices of long discussions and high proportion of review disagreement in code reviews were found to increase design degradation. Finally, we also discuss how the study findings shed light on how to improve the research and practice of modern code review.

Currently, due to ease of acquisition of mobile devices and their increasing processing power and storage, many applications in various areas and services have been developed. With access to information held by Internet, many data and research institutions are accessible in repositories in popular formats and they are made available for use by users and developers, such as the Brazilian Portal of Open Data. Due to the transport area is a recurring theme in the Brazilian daily, causing many disorders in big cities, we focus in the the vehicle fleet visualization of Brazilian capital. In this sense, this article aims to propose an application for mobile devices, called cARFleet, to open data visualization. In the application were incorporated features of augmented reality for better visualization and differentiation of the tool.

For Dynamic Software Product Lines (DSPLs), evaluating the quality of a feature model is important to ensure that errors in the early stages do not spread throughout the DSPL. Measures extracted from feature models have been proved to be useful in the quality evaluation of such models. However, the process used for computing the values of these quality measures for a large set of feature models can be cumbersome and error prone. To cope with this problem, we present DyMMer, a tool to support the automatic extraction of quality measures from feature models in DSPLs. After that, we can analyse the results and propose improvements for the feature models. Currently, the DyMMer tool is able to collect 40 different quality measures from a DSPL feature model.