Abstract
Robotics in education has special relevance in current digital society where students should know how to deal with technology. In this paper, it is presented an educational experiment in the mobile robotics domain. The referred experiment was part of a summer camp, which took place at the Polytechnic Institute of Bragança Portugal, being its technological aspects related with mobile robotics. Other than the technological aspects, the students participated in many different cultural and social activities, having the opportunity to know the city of Bragança and also to know different persons, mainly students, professors, researchers and laboratory technicians. The applied approach in the summer camp was a challenge based learning methodology, being involved in the experiment 3 professors, 4 monitors, working with a group of 16 secondary school students. The described experiment was planned as an activity of the RoboSTEAM - Integrating STEAM and Computational Thinking development by using robotics and physical devices ERASMUS+ Project. © 2019 ACM.

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Software analytics supports data-driven decision making, which allows software practitioners to leverage valuable insights from software data to improve their processes and many quality aspects of the software. In this paper, we present an artifact designed from a set of patterns to support agile teams to plan and manage software analysis activities, named Software Analytics Canvas. Further, we report the study undertaken to evaluate the ease of use and the utility of our canvas from the practitioners' viewpoint, and a participatory design session carried out to collect information about possible artifact improvements. In general, subjects found the artifact useful, but some of them reported difficulties in learning and understanding how to use it. In the participatory design, they pointed out improvement points and a new layout for the canvas components. The results of both studies helped us refine the proposed artifact, improving both the terms used in each element and the layout of the blocks to make more sense for its users.

Abstract
In principle, Model-Driven Engineering (MDE) addresses central aspects of robotics software development. Domain experts could leverage the expressiveness of models; implementation details over different hardware could be handled by automatic code generation. In practice, most evidence points to manual code development as the norm, despite several MDE efforts in robotics. Possible reasons for this disconnect are the wide ranges of applications and target platforms making all-encompassing MDE IDEs hard to develop and maintain, with developers reverting to writing code manually. Acknowledging this, and given the opportunity to leverage a large corpus of open-source software widely adopted by the robotics community, we pursue modeling as a complement, rather than an alternative, to manually written code. Our previous work introduced metamodels to describe components, their interactions, and their resulting composition, as inspired by, but not limited to, the de-facto standard Robot Operating System (ROS). In this paper we put such metamodels into use through two contributions [1]. First, we automate the generation of models from manually written artifacts through extraction from source code and runtime system monitoring. Second, we make available an easy-to-use web infrastructure to perform the extraction, together with a growing database of models so generated. Our aim with this tooling, publicly available both as-a-service and as source code, is to lower the MDE barrier for practitioners and leverage models to 1) improve the understanding of manually written code; 2) perform correctness checks; and 3) systematize the definition and adoption of best practices through large-scale generation of models from existing code. A comprehensive example is provided as a walk-through for robotics software practitioners.

Abstract
The continuous adoption of Additive Manufacturing (AM) can enhance Supply Chain's (SC) effectiveness, adaptability and competitiveness. AM allows for a decentralized SC, bringing production centres nearer to customers, increasing products availability and decreasing inventory level and lead time. However, the integration of SC and AM brings difficulties, leading to the need of a completely new SC design. This paper proposes an optimization model supporting the design of spare parts SCs operating under a Make-To-Order (MTO) strategy. The proposed approach considers the decision of deploying productive resources (3D printers) in locations of a spare parts SC. The problem is represented as a combination of the p-median and location-allocation optimization models, which are solved using a Mixed Integer Linear Programming (MILP). The approach is tested in two scenarios from a real-world use case of an elevator maintenance service provider. Obtained results demonstrated the promising capabilities of the proposed approach for handling the new design challenges arising from the forthcoming widespread use of 3D printers in manufacturing SCs.