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Context: Software team formation is an important project management activity. However, forming appropriate teams is a challenge for most of the companies. Objective: To analyze and synthesize the state of the art on the software team formation research. Additionally, we aim to organize the identified body of knowledge in software team formation as a taxonomy. Method: Using a Snowballing-based systematic mapping study, 51 primary studies, out of 2516, were identified and analyzed. We classified the studies considering the research methods used, their overall quality, and the characteristics of the formed teams and the proposed solutions. Results: The majority of the studies use search and optimization techniques in their approaches. Also, technical attributes are the most frequent type considered to build individuals' profiles during the team formation process. Furthermore, we proposed a taxonomy on software team formation. Conclusion: There is a predominant use of search-based approaches that combine search and optimization techniques with technical attributes. However, the adoption of non-technical attributes as complementary information is a tendency. Regarding the research gaps, we highlight the level of subjectivity in software team formation and the lack of scalability of the proposed solutions.

Context-Agile Software Development (ASD) and Reuse-Driven Software Engineering (RDSE) are wellaccepted strategies to improve the efficiency of software processes. A challenge to integrate both approaches is that ASD relies mostly on tacit knowledge, hampering the reuse of software development assets. An opportunity to enable RDSE for ASD is by improving the traceability between user stories (USs), the most used notation to register product requirements in ASD. Having enough link semantics between USs could enable defining similarity between them and, consequently, promote RDSE for ASD. However, this is an open challenge. Objective-To propose a taxonomy for adding link semantics between USs, focusing on easing the task of identifying similar ones. Such links, with support of traceability tools, enable the reuse of USs and their related assets. Method: We constructed a taxonomy for types of US focusing on Web Information Systems. The taxonomy is used to classify the US, given two facets: module and operation. Such information is used to infer the similarity between USs using link rules. We developed the taxonomy based on an empirical analysis of five product backlogs, containing a total of 118 USs. Afterward, we validated the taxonomy in terms of its potential to enable the reuse of US-related assets. First, we executed an offline validation by applying it to classify 530 USs from 26 already ended projects. Finally, we applied the taxonomy in a case study with two ongoing projects (59 USs). Results: The proposed taxonomy for USs is composed of two sub-facets, namely, module and operation, which have, respectively, three and 18 terms. In terms of coverage, for the offline study and case study, we classified 90.17% of the USs with the proposed taxonomy. For the case study, we classified all the USs analyzed. Conclusion: We concluded that it is possible to use our approach to compare USs and, consequently, retrieve their related assets. Our results regarding its practical utility have shown that users considered the taxonomy a useful approach to ease the process of assessing the similarity between user stories.

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Diabetes mellitus is a chronic disease that is considered a worldwide epidemic, and its control is a constant challenge for health systems. Since insulin had its first successful use, scientists have researched to improve the desired effects and reduce side-effects. Over the years, the challenge has been to increase adherence to treatment and improve the quality of life for diabetics by developing an insulin delivery system. This systematic review (SR) analyses experimental articles from 1998 to 2018 related to the development of the chitosan/insulin delivery system (CIDS). Automated support: Start tool was used to perform part of these activities. The search terms “insulin”, “delivery or release system”, and “chitosan” were used to retrieve articles in PubMed, Science Direct, Engineering Village, and HubMed. A total of 55 articles were selected. The overview, phase, model, way of administration, and the efficiency of CIDS were analyzed. According to SR results, most of the articles were published from 2010 onwards, representing 72.7% of the selected papers, and research groups from China publicized 23.6% of the selected articles. According to the SR, 51% of the studies were carried out in vivo and 45% in vitro. Most of the systems were nanoparticle based (54.8%), and oral administration was proposed by 60.0% of the selected articles. Only 36.4% performed loaded capacity and encapsulation efficiency assays, and 24 h (16.4%), 12 h (12.7%), and 6 h (11.0%) were the most frequent insulin release times. Chitosan’s intrinsic characteristics, which include biodegradability, biocompatibility, adhesiveness, the ability to open epithelial tight junctions to allow an increase in the paracellular transport of macromolecular drugs, such as insulin, and the fact that it does not result in allergic reactions in the human body after implantation, injection, topical application or ingestion, have contributed to the increase in research of CIDS over the years. However, the number of studies is still limited and the use of an alternative form of insulin administration is not yet possible. Thus, more studies in this area, aiming for the development of an insulin delivery system that can promote more adherence to the treatment and patient comfort, are required.

Background:The literature presents distinct models to assess the Teamwork Quality (TWQ) for agile teams. These models have different constructs and, consequently, measures. Unfortunately, there are no results of empirical studies contrasting the existing models. Goal: To fill this gap, this study aims to provide a deep insight into how two state-of-the-art TWQ models (i.e., instruments) compare to each other with respect to the calculated results for practical use. Method: We performed an empirical study to compare both models in terms of their constructs and measures. For comparing their constructs, we ranked the variables from both models, given their relative impact on teamwork quality, and compared the ranks. For comparing their measures, we collected data using both instruments by interviewing 158 team members from two software development companies. First, we theoretically mapped the variables from both models, given their definition. Afterward, from the collected data, we calculated each of the models' variables using the procedures proposed by their creators. Then, we analyzed the level of agreement between the models for each variable, using the Mean Relative Error (MRE). Results: In terms of the constructs, for practical purposes, both models are equivalent, except for including a variable for Team Autonomy. For the measures, the instruments produced similar results for five variables (i.e., Communication, Coordination, Balance of Member Contribution, Effort, and Cohesion). For the sixth variable (Mutual Support), we presented evidence that the results are similar in the models. Still, we believe that more research is needed to analyze it. Conclusions: We believe that we

Multiple models (or instruments) for measuring Teamwork Quality (TWQ) for Agile Software Development can be found in the literature. Regardless, such models have different constructs and measures, with no empirical evidence for comparing them. This study analyzed two agile TWQ models, resulting in equivalent results. We mapped the models' variables given their definitions. We then collected data using both a Bayesian Network model, namely the TWQ-BN model, and Structural Equation Modeling, namely the TWQ-SEM model. We interviewed 162 team members from two software development companies. We analyzed the data using the Bland-Altman method. We obtained enough evidence to conclude that the results for Communication, Coordination, Cohesion and Mutual Support variables are not equivalent. On the other hand, we did not have enough evidence to claim that the models do not agree for measuring Effort and Balance of member contribution variables. The results of this study detail how two state-of-the-art agile TWQs compare in terms of their measures as well as potential research areas for further investigation.

Technical Debt (TD) refers to the consequences of taking shortcuts when developing software. Technical Debt Management (TDM) becomes complex since it relies on a decision process based on multiple and heterogeneous data, which are not straightforward to be synthesized. In this context, there is a promising opportunity to use Intelligent Techniques to support TDM activities since these techniques explore data for knowledge discovery, reasoning, learning, or supporting decision-making. Although these techniques can be used for improving TDM activities, there is no empirical study exploring this research area. This study aims to identify and analyze solutions based on Intelligent Techniques employed to support TDM activities. A Systematic Mapping Study was performed, covering publications between 2010 and 2020. From 2276 extracted studies, we selected 111 unique studies. We found a positive trend in applying Intelligent Techniques to support TDM activities, being Machine Learning, Reasoning Under Uncertainty, and Natural Language Processing the most recurrent ones. Identification, measurement, and monitoring were the more recurrent TDM activities, whereas Design, Code, and Architectural were the most frequently investigated TD types. Although the research area is up-and-coming, it is still in its infancy, and this study provides a baseline for future research. CCS CONCEPTS• General and reference → Empirical studies.