Abstract Requirements engineering is crucial for software development. Analyzing large sets of requirements in natural language is difficult and time-consuming, especially in Internet of Things (IoT) projects, where heterogeneous devices, services, communication infrastructures, and environmental constraints increase the complexity of the requirements. In this context, clustering can support requirements analysis by organizing functional requirements into semantically coherent groups that represent the same functional aspect or subsystem. This helps analysts reduce manual effort, identify relationships and dependencies between requirements, structure large sets of requirements, and better understand system responsibilities and specific IoT concerns. For these reasons, we propose ReqCluster4IoT, a method for clustering functional requirements in IoT applications. The method builds on a focused exploration of the literature to identify IoT characteristics, along with experiments on requirement classification and text semantic similarity. We evaluated the method’s acceptance and usefulness through an online questionnaire and a focus group. The online questionnaire collected feedback from 10 IoT software developers, whereas the focus group provided more in-depth insights into the method’s results. Findings suggest that ReqCluster4IoT can capture meaningful relationships among requirements and generate clusters that practitioners perceive as useful. Additionally, the method helps practitioners identify how related functionalities are organized into clusters, reveal relationships among requirements, and associate each cluster with IoT-specific characteristics, thereby supporting requirements analysis, implementation planning, and requirements management. Overall, participants reported positive acceptance of the method regarding its usefulness, its potential to reduce effort, and their intention to use it.

The software ecosystem strategy dominated the digital games industry. The leading game companies adopted ecosystem dynamics, forcing external actors (or developers) to adapt to a highly competitive scenario. Given the Brazilian digital games industry's growth, this study aims to characterize and understand the game software ecosystem scenario. We conducted an online survey research to identify positives, negatives, and opportunities from different ecosystem actors. The survey received 200 responses from different actors from both industry and academia. Three researchers applied qualitative analysis procedures to extract codes and relationships from the answers. We identify twelve aspects (i.e., network, financial sustainability, market opportunity etc.) and their relationships that describe the Brazilian ecosystem. We also investigated these aspects in three analysis groups (i.e., positives, negatives, and opportunities) and compared them against the Brazilian games censuses. Next, we classified codes based on the software ecosystem dimensions (i.e., technical, business, and social) to point out research needs. The codes' relationships allowed us to understand some concerns about the Brazilian context. The qualitative analysis procedures and the discussion on the software ecosystem dimensions provide an overview of a complex scenario, including a list of aspects to support further academic research and industrial techniques.

Digital Phenotyping of Mental Health (DPMH) aims to passively collect data from ubiquitous devices to be used as evidence in the process of diagnosis, treatment, and monitoring. Literature presents different sensing mobile applications for digital phenotyping, however they are not extensible and can not be customized for use in other research. In this paper, we propose OpenDPMH, a framework for developing mobile sensing applications able to collect contextual data in order to produce useful user information that represent situations of interest for mental health professionals and researchers, such as human behaviors and habits. Our solution is extensible and reusable, as it allows the inclusion of modules for collecting and processing new raw context data with features for data distribution. By implementing a case study, we demonstrate that OpenDPMH is suitable for the development of DPMH mobile applications. Moreover, we carried out experiments to evaluate the energy consumption on smartphones, which demonstrate a low battery cost to run applications developed using the proposed framework.

Depression is a mental disorder characterized by persistent sadness, loss of interest, and a set of behavioral changes. The high prevalence of depression imposes a significant burden on the world population, demanding methods capable of monitoring and treating this mental disorder. Currently, a large number of mobile applications have been designed to provide support to depressive people. This paper aims to identify, analyze and characterize the current state of mobile applications focused on depression. To do so, we conducted a systematic review of applications for depression assistance. The two most popular mobile app stores (Google Play Store and Apple App Store) have been explored to find the most relevant apps. After applying the inclusion and exclusion criteria and performing the quality assessment of the results, 216 applications were selected for the data extraction phase, where we summarized their benefits and limitations and identified gaps and trends. The results of this review evidenced that there is a growth in the diversity of apps' purposes such as chatbot, online therapy, educational tools, mood tracker, testing, and self-help.

Clinical scores are a widely discussed topic in health as part of modern clinical practice. In general, these tools predict clinical outcomes, perform risk stratification, aid in clinical decision making, assess disease severity or assist diagnosis. However, the problem is that clinical scores data are traditionally obtained manually, which can lead to incorrect data and result. In addition, by collecting biological/health data in real time from humans, the current mobile health (mHealth) solutions that computationally solve that problem are limited because those systems are developed considering the specificities of a single clinical score. This work is part of the MDD4ClinicalScores project that addresses the productivity in developing mHealth solutions for clinical scores through the use of Model Driven Development concepts. This paper focus in describing DSML4ClinicalScore, a high-level domain-specific modeling language that uses the Ecore metamodel to describe a clinical score specification. To propose the DSML4ClinicalScore we analysed 89 clinical scores to define the artifacts of this proposed Metamodel. In the end, a practical case study using this DSML is provided to validate the DSML4ClinicalScore Metamodel, and to show how to use the proposal in a clinical situation scenario.

The usage of Learning Management Systems (LMS) based on innovative Information and Communication Technologies (ICT) as Edmodo has been established a way to break up the frontiers in education of Brazilian classrooms. Such LMS have learning strategies as forum, chats, and news feed. These strategies are similar to social networks and they are used by students in several educational levels. Moreover, the LMS should motivate the interaction between professors and students through virtual scenario. This paper presents the perceptions of the LMS application, the Edmodo, as a tool of improve the outside classroom relationship between professors and students. We apply the LMS in undergraduate computer courses. As results, we verify that students access the LMS to obtain courses' slides, to keep in touch with professors, and to answer tests questions regarding specific courses' topic. The LMS was easy to use and was important for sending urgent class information (like data of class exams and class cancels) and for expanding the activities' deadlines. Moreover, the Edmodo helps the professors to send feedbacks regarding the students' activities and support the upload of activities by the students. Finally, the students point out that the Edmodo can by apply in other undergraduate courses and can facilitate the interdisciplinary of courses because the LMS has a social network of professors and students. The application of LMS is relevant to support the activities out of classroom, in special of undergraduate scenario.

Several studies have shown that both individual and team capability can affect software development performance and project success; a deeper understating of such phenomena is crucial within the context of Agile Software Development (ASD), given that its workforce is a key source of agility. This paper contributes towards such understanding by means of a case study that uses data from 14 interviews carried out at a large telecommunications company, within the context of a mobile money transfer system developed in Sweden and India, to identify individual and team capability measures used to form productive teams. Our results identified 10 individual and five team capability measures, of which, respectively, five and four have not been previously characterised by a systematic literature review (SLR) on this same topic. Such review aggregated evidence for a total of 133 individual and 28 team capability measures. Further work entails extending our findings via interviewing other software/software-intensive industries practicing ASD.

Software-intensive systems have become increasingly ubiquitous, large, and complex, with dissemination in several application domains and tightly dependent upon different technologies. Such systems are usually centered in a software platform so that increasing attention has been paid to influence and interdependency in relationships among all the involved players, forming software ecosystems (SECO). Despite the initial advances in SECO research, few analytical models, case studies with real data, and integrated tool support exist. A great barrier for the evolution of the field towards aiding decision-making in the real industry is the gap lack in theof SECO modeling support. In this paper, we aim to perform an exploratory study on the need for modeling in the SECO field. We preliminarily identified some modeling elements from the SECO literature and explored them in the context of a real SECO in the educational domain.

In the area of requirements engineering there are elicitation techniques that are based on business context to identify the software requirements. However, these techniques do not commonly use business processes models as a source of information relevant to the elicitation of requirements. Such techniques extract use cases from business processes models. However, before modeling use cases, it is important to produce a document with functional and non-functional requirements and business rules. Therefore, this work proposes a new technique to aid extracting the requirements from business process diagrams: the REMO (Requirements Elicitation oriented by business process MOdeling) technique. This paper shows that, through the use of empirical studies, it was possible to obtain results that contributed to the evolution of the REMO technique. Additionally, the paper describes the qualitative analysis of the second empirical study, which used the procedures of two different methods: the Technology Acceptance Model (TAM) and Grounded Theory.

Background: Understanding how to successfully deal with human and technical aspects involved in Software Process Improvement (SPI) programs is a challenging issue. Technical aspects are not enough to guarantee the success of activities. Human factors (e.g., experiences, opinions and perceptions) impact the effectiveness of SPI programs. Aim: this paper aims to improve our current understanding on how human aspects can influence SPI programs from the point of view of the professionals involved in such efforts. We compare our findings with the results of previous studies in order to analyze the different contexts in which human aspects influence SPI. Method: We conducted a qualitative study in the context of small companies involved in SPI programs in Amazonas State in Brazil based on the Brazilian Maturity Model - MPS.BR. We used semi-structural interviews with software engineers. Results: We identified findings about eleven of the fourteen investigated human aspects. We identified new characteristics that motivate actors to get involved in SPI programs. Finally, while previous research indicated that the process of individual decision making as a negative aspect, we identified this as a positive aspect. Conclusions: Despite the differences in companies' size and maturity models, our findings corroborate many of the important results of previous case studies. The consistency of the presented results with previous research in different organizations, teams, and countries suggest that these results are true for software engineering in general. However, there were differences related to the motivating factors and the perception of the individual decision making. In our study, we observed that the personality, perception and employee selection aspects were also significant to the SPI program's success. This has not been re