A Unified Modeling Language (UML) tem sido ensinada em grande parte dos cursos de (pós-)graduação em Ciência da Computação, especialmente naqueles com ênfase em Engenharia de Software. No entanto, pouco se sabe sobre o alinhamento entre como a UML tem sido ensinada e como ela tem sido utilizada na indústria de software. Este artigo apresenta os resultados de dois surveys: um com 23 docentes sobre como a UML tem sido ensinada em Instituições de Ensino Superior de Maringá e Região e um com 43 profissionais da mesma região sobre como a UML tem sido aprendida e usada na prática. Os resultados são discutidos e fornecem suposições e direções para melhorar o ensino de UML e atender às necessidades reais do mercado.

Background:] Experimentation in Software Engineering plays a central role on sharing and verifying scientific findings. As experiments have increased significantly in Software Engineering area, we observe that most of them fail to provide a way to be repeated, replicated or reproduced, thus jeopardizing or delaying the evolution of the Software Engineering area. [Aims:] In this vision paper, we present and discuss techniques and infrastructure to continuously improve experiments towards repeatability, replicability, and reproducibility. [Method:] We define these techniques and infrastructure based on experiences of our research groups and existing literature. Furthermore, we follow Open Science principles. [Results:] We provide incipient results and foresee a central infrastructure composed of two repositories and two recommendation systems to support techniques for: reporting experiments; developing ontologies for experiments and open educational resources; mining and recommending experiments; specifying data management plans, identifying families of experiments; and teaching and learning experimentation. [Conclusions:] Our techniques and infrastructure will prospectively motivate and benefit Software Engineering evolution by improving the conduction and further reproducibility of experiments. CCS CONCEPTS• Software and its engineering → Empirical software validation;

Open Data is one of the main concepts of Open Science, which has the purpose to make scientific research artifacts accessible for everyone. Open data provides recommendations and practices to get access and use data from scientific researches, in a free, permanent, citable, auditable and interchangeable way. To facilitate the data management, it is important to store them in a repository. Considering this context, this paper provides a comparison among five known open data repositories. We performed the comparison taking into account a set of criteria, such as, data format constraints, digital identifier, versioning of published datasets, curators of data collections, metadata schema, versioning and exportation, storage limit, paid services, redundancy and preservation, access controls and APIs. We present results and discussions, in terms of such criteria.

In this vision paper, we present how Open Science practices can be adopted to prospectively support promoting software engineering controlled experiments and quasi-experiments. As experimentation in software engineering has gained extraordinary attention and increased in the last decade, we as com- munity should focus on the openness of experiment artifacts and processes to every citizen, especially those artifacts produced with public and government funding. Such openness might bring several benefits towards evolving this area based on well-reported experiments, artifacts, processes, shared data, and experiences gathered up. In view of this, we envision an open science framework for software engineering controlled experiments and quasi-experiments. In addition, we provide a research agenda, which is intended to be accomplished in the next five years.

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