Abstract

Abstract
Researchers are being prompted by funders and institutions to expose the variety of results of their projects and to submit a Data Management Plan as part of their funding requests. In this context, institutions are looking for solutions to provide support to research data management activities in general, including DMP creation. We propose a collaborative approach where a researcher and a data steward create a DMP, involving other parties as required. We describe this collaborative method and its implementation, by means of a set of case studies that show the importance of the data steward in the institution. Feedback from researchers shows that the DMP are simple enough to lead people to engage in data management, but present enough challenges to constitute an entry point to the next level, the machine-actionable DMP.

Abstract
The significant evolution of smartphones has given ordinary people the power to create good-quality content which can then be spread, by the press, over multiple platforms. Citizens are almost always the first ones to arrive at a breaking news location and can provide the initial images of the scene. However, existing crowdsourced tools and platforms are predominantly centralized and are usually fed with unreliable and untrustworthy information. This work introduces a Crowd Journalism ecosystem whose core is a video marketplace web tool based on an organization-level decentralized system that can store, visualize, rate, and execute transactions of live-made videos. Smart contracts ensure that all the transactions are transparent and secure. This approach to Crowd Journalism exploits the inherent features of a blockchain such as offering trustful, anonymized, and immutable transactions, which has the potential to revolutionize the way news content is shared and commercially exploited.

Abstract
Internet-of-Things systems are comprised of highly heterogeneous architectures, where different protocols, application stacks, integration services, and orchestration engines co-exist. As they permeate our everyday lives, more of them become safety-critical, increasing the need for making them testable and fault-tolerant, with minimal human intervention. In this paper, we present a set of self-healing extensions for Node-RED, a popular visual programming solution for IoT systems. These extensions add runtime verification mechanisms and self-healing capabilities via new reusable nodes, some of them leveraging meta-programming techniques. With them, we were able to implement self-modification of flows, empowering the system with self-monitoring and self-testing capabilities, that search for malfunctions, and take subsequent actions towards the maintenance of health and recovery. We tested these mechanisms on a set of scenarios using a live physical setup that we called SmartLab. Our results indicate that this approach can improve a system’s reliability and dependability, both by being able to detect failing conditions, as well as reacting to them by self-modifying flows, or triggering countermeasures.

Abstract
Evermore end-to-end digital services depend on the proper interoperation of multiple products, forming a distributed system, often subject to timing requirements. To ensure interoperability and the timely behavior of such systems, it is important to conduct integration tests that verify the interactions with the environment and between the system components in key scenarios. The automation of such integration tests requires that test components are also distributed, with local testers deployed close to the system components, coordinated by a central tester. Test coordination in such a test architecture is a big challenge. To address it, in this article we propose an approach based on the pre-processing of the test scenarios. We first analyze the test scenarios in order to check if conformance errors can be detected locally (local observability) and test inputs can be decided locally (local controllability) by the local testers for the test scenario under consideration, without the need for exchanging coordination messages between the test components during test execution. If such properties do not hold, we next try to determine a minimum set of coordination messages or time constraints to be attached to the given test scenario to enforce those properties and effectively solve the test coordination problem with minimal overhead. The analysis and enforcement procedures were implemented in the DCO Analyzer tool for test scenarios described by means of UML sequence diagrams. Since many local observability and controllability problems may be caused by design flaws or incomplete specifications, and multiple ways may exist to enforce local observability and controllability, the tool was designed as a static analysis assistant to be used before test execution. DCO Analyzer was able to correctly identify local observability and controllability problems in real-world scenarios and help the users fix the detected problems.

Abstract
High-maturity software development processes and development environments with automated data collection can generate significant amounts of data that can be periodically analyzed to identify performance problems, determine their root causes, and devise improvement actions. However, conducting the analysis manually is challenging because of the potentially large amount of data to analyze, the effort and expertise required, and the lack of benchmarks for comparison. In this article, we present ProcessPAIR, a novel method with tool support designed to help developers analyze their performance data with higher quality and less effort. Based on performance models structured manually by process experts and calibrated automatically from the performance data of many process users, it automatically identifies and ranks performance problems and potential root causes of individual subjects, so that subsequent manual analysis for the identification of deeper causes and improvement actions can be appropriately focused. We also show how ProcessPAIR was successfully instantiated and used in software engineering education and training, helping students analyze their performance data with higher satisfaction (by 25%), better quality of analysis outcomes (by 7%), and lower effort (by 4%), as compared to a traditional approach (with reduced tool support).