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.

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.

Technical Debt (TD) is a metaphor reflecting technical compromises that can yield short-term benefits but might hurt the long-term health of a software system. With the increasing amount of data generated when performing software development activities, an emergent research field has gained attention: applying Intelligent Techniques to solve Software Engineering problems. Intelligent Techniques were used to explore data for knowledge discovery, reasoning, learning, planning, perception, or supporting decision-making. Although these techniques can be promising, there is no structured understanding related to their application to support Technical Debt Management (TDM) activities. Within this context, this study aims to investigate to what extent the literature has proposed and evaluated solutions based on Intelligent Techniques to support TDM activities. To this end, we performed a Systematic Mapping Study (SMS) to investigate to what extent the literature has proposed and evaluated solutions based on Intelligent Techniques to support TDM activities. In total, 150 primary studies were identified and analyzed, dated from 2012 to 2021. The results indicated a growing interest in applying Intelligent Techniques to support TDM activities, the most used: Machine Learning and Reasoning under uncertainty. Intelligent Techniques aimed to assist mainly TDM activities related to identification, measurement, and monitoring. Design TD, Code TD, and Architectural TD are the TD types in the spotlight. Most studies were categorized at automation levels 1 and 2, meaning that existing approaches still require substantial human intervention. Symbolists and Analogizers are levels of explanation presented by most Intelligent Techniques, implying that these solutions conclude a general truth after considering a sufficient number of particular cases. Moreover, we also cataloged the empirical research types, contributions, and validation strategies described in primary studies. Based on our findings, we argue that there is still room to improve the use of Intelligent Techniques to support TDM activities. The open issues that emerged from this study can represent future opportunities for practitioners and researchers.

Organizations have used software engineering data to support decision-making by applying data-driven approaches such as software analytics. However, adopting analytics tools depends on the information they provide and the real needs of practitioners. Significant research has addressed the needs of developers, whereas the needs of managers are not well understood. Moreover, few studies have focused on the practitioners’ view of data-driven decision-making. From a managerial viewpoint, this case study provides an in-depth analysis of the information needs and the perceptions of data-driven decision-making of practitioners from one software development organization. We interviewed personnel in leadership positions and used coding procedures (open and selective coding) to analyze the collected data. We identified 19 software analytics use cases and mapped them to the software life cycle processes from ISO/IEC/IEEE 12207:2017, of which organizational project-enabling and technical management processes were the most highlighted by the interviewees. We also provided a set of indicators to meet the identified use cases and shed light on critical aspects of the organization’s analytics scenario. Furthermore, we identified project-related, human-related, and context-specific factors that affect managerial decision-making and organizational aspects that influence the adoption of software analytics initiatives. Although our results are particularly relevant to organizations similar to the one described herein, they aim to serve as input for implementing new analytics solutions by practitioners and researchers in general and contribute to the body of knowledge on the topic from a practitioner’s perspective, helping organizations in their attempts to adopt data-driven approaches.

(Context) Code smells indicate poor coding practices or design flaws, suggesting deeper software quality issues. While addressing code smells promptly improves software quality, traditional detection techniques often fail in continuous detection during software development. (Problem Statement) More recently, Interactive Detection (ID) technique has been proposed, enabling the detection of code smells continuously. Although the use of this technique by developers and organizations is promising, there are no practical recommendations for its use in the context of software development. (Goal) The objective of this study was to propose and evaluate the integration of ID into the widely adopted Scrum framework for agile software development. (Method) To achieve this objective, we utilized a mixed-method approach that combined a comprehensive literature review and expert knowledge to propose the integration. Furthermore, we conducted a focus group and a controlled experiment involving software development activities to evaluate this integration. (Results) The findings revealed that this integration significantly benefitted software development, such as early detection of code smells, increased effectiveness in code smell detection, and improved code quality. These findings shed light on the potential benefits of adopting this integration, offering valuable insights for developers and researchers. (Conclusions) This research emphasized the importance of continuous code smell detection as an integral part of agile development and opened avenues for further research in code quality management within agile methodologies.