Internet of Things (IoT) resources cooperate with themselves for requesting and providing services. In heterogeneous and complex environments, those resources must trust each other. On-Off attacks threaten the IoT trust security through nodes performing good and bad behaviors randomly, to avoid being rated as a menace. Some countermeasures demand prior levels of trust knowledge and time to classify a node behavior. In some cases, a malfunctioning node can be mismatched as an attacker. In this paper, we introduce a smart trust management method, based on machine learning and an elastic slide window technique that automatically assesses the IoT resource trust, evaluating service provider attributes. In simulated and real-world data, this method was able to identify On-Off attackers and fault nodes with a precision up to 96% and low time consumption.

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.

Trusted Execution Environments have been applied to improve data security in many distinct application scenarios since they enable data processing in a separate and protected region of memory. To investigate how this technology has been applied to the different IoT scenarios, which commonly deal with specific characteristics such as device resource constraints, we carried out a systematic literature review. For this, we selected and analyzed 58 papers from different conferences and journals, identifying the main IoT solutions and scenarios in which TEE has been employed.We also gathered the mentioned TEE advantages and disadvantages as well as the suggestions for future works. This study gives a general overview of the use of TEEs for cloud/fog-based IoT applications, bringing some challenges and directions.

Forming effective teams is an essential but challenging task, especially for organizations that carry out multiple projects simultaneously, a problem known as the Multiple Team Formation (MTF) problem. The literature presents several solutions for the MTF problem, mostly modeling it as a search problem. However, the existing solutions are not suitable for Scrum projects. We addressed this gap by developing an approach composed of two main steps. First, we designed a Structured Task Model to support creating developers’ profiles given their performance on past Scrum projects. Then, given a set of target projects’ technology requirements and the available developers’ profiles, we developed a Genetic Algorithm to form the teams for a set of target projects. We evaluated the proposed approach by comparing the teams formed by our approach with the ones formed by project managers from one organization. Our approach achieved 85% of precision when compared with the teams provided by the project managers who worked on the same target projects. We also recorded an acceptance rate of up to 75%. The significant value of precision achieved suggests that our approach can provide teams close to the project managers’ expectations. In addition, our Structured Task Model offers a promising way to build technical profiles semi-automatically for Scrum developers. In future work, we intend to investigate how to complement the developers’ profiles by using other types of attributes and knowledge sources.

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.