Ademar Aguiar

Abstract
Misinformation became pervasive on social media applications. The companies behind this kind of system have launched tools to avoid the problem, but some issues regarding the user behavior and proper software quality still need a forceful approach. First attempts to mitigate misinformation did not take into account user behavior and softwares requirements like learnability and accuracy, furthermore the characteristics of actors and artifacts from social media applications ecosystem has not been explored. This research aims to evaluate the usability of available tools made to combat the spread of misinformation and to verify the interrelationship between actors and artifacts from social media applications ecosystem for suggesting improvements on development of these tools. © 2018 AISTI.

Abstract
Cloud software continues to expand globally, highly motivated by how widespread the Internet is and the possibilities it unlocks with cloud computing. Still, cloud development has some intrinsic properties to it, making it complex to unexperienced developers. This research is capturing those intricacies in the form of a pattern language that gathers ten patterns for engineering software for the cloud. This paper elaborates on that research by contributing with two new patterns: Automated Recovery, which checks if a container is working properly, automatically recovering it in case of failure and Scheduler, which periodically executes actions within the infrastructure. The described patterns are useful for anyone designing software for the cloud, either to bootstrap or to validate their design decisions with the end goal of enabling them to create better software for the cloud. © 2018 Copyright held by the owner/author(s).

Abstract
Cloud software continues to expand globally, highly motivated by how widespread the Internet is and the possibilities it unlocks with cloud computing. Still, cloud development has some intrinsic properties to it, making it complex to unexperienced developers. This research is capturing those intricacies in the form of a pattern language, gathering ten patterns for engineering software for the cloud. This paper elaborates on that research by contributing with two new patterns: External Monitoring, which continuously monitors the system as a black box, validating its status and Failure Injection, which continuously verifies system reliability by injecting failures into the cloud environment and confirming that the system recovers from it. The described patterns are useful for anyone designing software for the cloud, either to bootstrap or to validate their design decisions with the end goal of enabling them to create better software for the cloud. © 2018 Copyright held by the owner/author(s).

Abstract
Application frameworks are a powerful technique for large-scale reuse, but require a considerable effort to understand them. Good documentation is costly, as it needs to address different audiences with disparate learning needs. When code and documentation prove insuficient, developers turn to their network of experts. Nevertheless, this proves difficult, mainly due to the lack of expertise awareness (who to ask), wasteful interruptions of the wrong people and unavailability ( either due to intrusion or time constraints). The DRIVER platform is a collaborative learning environment where framework users can, in a non-intrusive way, store and share their learning knowledge while following the best practices of framework understanding (patterns). Developed by the authors, it provides a framework documentation repository, mounted on a wiki, where the learning paths of the community of learners can be captured, shared, rated, and recommended. Combining these social activities, the DRIVER platform promotes collaborative learning, mitigating intrusiveness, unavailability of experts and loss of tacit knowledge. This paper presents the assessment of DRIVER using a controlled academic experiment that measured the performance, effectiveness and framework knowledge intake of MSc students. The study concluded that, especially for novice learners, the platform allows for a faster and more effective learning process.

Abstract