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Cyber-physical systems (CPS) and IoT systems are nowadays commonly designed as self-adaptive, endowing them with the ability to dynamically reconfigure to reflect their changing environment. This adaptation concerns also the security, as one of the most important properties of these systems. Though the state of the art on adaptivity in terms of security related to these systems can often deal well with fully anticipated situations in the environment, it becomes a challenge to deal with situations that are not or only partially anticipated. This uncertainty is however omnipresent in these systems due to humans in the loop, open-endedness and only partial understanding of the processes happening in the environment. In this paper, we partially address this challenge by featuring an approach for tackling access control in face of partially unanticipated situations. We base our solution on special kind of aspects that build on existing access control system and create a second level of adaptation that addresses the partially unanticipated situations by modifying access control rules. The approach is based on our previous work where we have analyzed and classified uncertainty in security and trust in such systems and have outlined the idea of access-control related situational patterns. The aspects that we present in this paper serve as means for application-specific specialization of the situational patterns. We showcase our approach on a simplified but real-life example in the domain of Industry 4.0 that comes from one of our industrial projects.

Sustainability is not only understood as a manner to safeguard the environment, but also to fight against injustices and inequalities that exist on social and economic level. One of the biggest challenges that exists in social sustainability is to achieve gender equality, as defended by the Sustainable Development Goal (SDG) 5 of the 2030 Agenda. But this is a complex challenge and must be addressed from different spheres and fields of knowledge. Artificial Intelligence (AI) has proven to be an essential asset in the development of new and innovative technologies. Its development, adoption, and constant use by a growing part of the world’s population demonstrates the social impact it entails and the importance of also becoming an asset for social sustainability and, in this case especially, for gender equality. That is why this study aims to collect the current knowledge about the fields of AI and gender equality, through the development of a Systematic Mapping Study (SMS) that identifies the most significant advances in this regard, as well as the main gaps that must be covered. The results and findings obtained in this work show the novelty of joint analysis of both areas, as well as increasing attention they have received in recent years. Likewise, they also demonstrate the need to address specific and urgent issues within gender equality, both in the field of AI and caused by its development.

Context: Several researchers have reported their experiences in applying secondary studies (Systematic Literature Reviews - SLRs and Systematic Mappings - SMs) in Software Engineering (SE). However, there is still a lack of studies discussing the value of performing secondary studies in an academic context. Goal: The main goal of this study is to provide an overview on the use of secondary studies in an academic context. Method: Two empirical research methods were used. Initially, we conducted an SM to identify the available and relevant studies on the use of secondary studies as a research methodology for conducting SE research projects. Secondly, a survey was performed with 64 SE researchers to identify their perception related to the value of performing secondary studies to support their research projects. Results: Our results show benefits of using secondary studies in the academic context, such as, providing an overview of the literature as well as identifying relevant research literature on a research area enabling to find reasons to explain why a research project should be approved for a grant and/or supporting decisions made in a research project. Difficulties faced by SE graduate students with secondary studies are that they tend to be conducted by a team and it demands more effort than a traditional review. Conclusions: Secondary studies are valuable to graduate students. They should consider conducting a secondary study for their research project due to the benefits and contributions provided to develop the overall project. However, the advice of an experienced supervisor is essential to avoid bias. In addition, the acquisition of skills can increase student's motivation to pursue their research projects and prepare them for both academic or industrial careers.

Software Engineering and Human-Computer Interaction look at the development process from different perspectives. They apparently use very different approaches, are inspired by different principles and address different needs. But, they definitively have the same goal: develop high quality software in the most effective way. The second edition of the workshop puts particular attention on efforts of the two communities in enhancing system quality. The research question discussed is: who, what, where, when, why, and how should we evaluate?

Several models and methodologies have been defined in order to support organisational process improvement. The implementation and institutionalisation of these approaches allow organisations to improve, mature, acquire and institutionalise best practices and management systems from multiple approaches. However, there are two issues, which have to be kept in mind. On one hand, it is possible to find several similarities amongst improvement, management and governance approaches. Experts and practitioners can thereby save, improve and optimize the organisational efforts using the best parts of existing models as building blocks; they can thus be prepared to deconstruct models, aiming for their designs to meet multiple needs. On the other hand, nonetheless, there are other factors which may influence, for example, compliance, or those aspects related to structural differences such as terminology, size, process, element structure, content, granularity, and complexity, which make difficult to work in multi-model environments. This being the case, the people involved need a map or guideline telling them how to carry out the harmonisation of models and standards that have to be implemented inside their organisations. In the quest to help support the work of harmonization of multiple models, this paper presents a framework that defines elements needed for the harmonization of multiple reference models to occur, as well as its application to three case studies. The results obtained show that the framework proposed has allowed the harmonization of several models. Diferentes modelos y metodologías se han definido para apoyar la mejora de procesos de las organizaciones. La implementación e institucionalización de estos enfoques permiten a las organizaciones mejorar, madurar, adquirir e institucionalizar las mejores prácticas y sistemas de gestión a partir de múltiples enfoques. Sin embargo, hay dos cuestiones que se deben tener en cuenta. Por un lado, es posible encontrar varias similitudes entre los enfoques de mejora, gestión y gobierno, de esta manera, expertos y profesionales pueden ahorrar, mejorar y optimizar los esfuerzos de la organización usando las mejores partes de los modelos existentes como bloques de construcción; en este sentido, tanto expertos como profesionales deben estar preparados para reconstruir los modelos con el objetivo de diseñar las prácticas de una organización de acuerdo a sus múltiples necesidades. Por otro lado, sin embargo, hay otros factores que pueden influir en el cumplimiento, lo que hace difícil para trabajar en entornos multimodelo, por ejemplo, los aspectos relacionados con las diferencias estructurales tales como la terminología, tamaño, estructura de elementos de proceso, contenido, granularidad y complejidad. En este sentido, las personas involucradas necesitan una ruta o guía que les permita saber cómo llevar a cabo la armonización de modelos y estándares que tienen que aplicar en sus organizaciones. Con el objetivo de facilitar la labor en la armonización de múltiples modelos, este artículo presenta un marco de trabajo que define los elementos necesarios para apoyar la armonización de múltiples modelos de referencia. Asimismo, se presenta su aplicación en tres estudios de caso. A partir de los resultados obtenidos ha sido posible armonizar múltiples modelos a través del marco de trabajo (framework) propuesto.

Open-source projects commonly receive new feature requests from different types of users from layperson end users to developers, who actively contribute code to the project. However, the submission of new feature requests and the processes adopted for handling them is not always clear. In this work, we aim at investigating the availability of the contribution information, and in particular on the new feature requests, on 66 out of the 100 most starred GitHub projects. We examined the contribution guidelines and other documentation from those 66 projects. We particularly searched for whether the projects openly welcomed new contributions, such as feature requests. Our finding shows that even the most starred GitHub projects are often not reporting information on how to contribute and, in particular, how new feature requests are managed. acceptedVersion

CNPq Experiments play an essential role in evaluating solutions in software engineering. A field of software engineering where experiments are frequently used is software development. In this field, many solutions are proposed to foster coding activities, such as different programming languages, developing techniques, tools, and other solutions. In this context, this research has two primary goals. The first goal is to investigate experiments performing coding activities (Coding Experiments). This investigation shall raise the most common characteristics of such experiments, and how current solutions supporting experiments address such coding context. The second goal is to propose a solution to support coding experiments according to their particular context characterization. This research was divided in many sub-steps. Each sub-step adopted distinct method. In the first step, we conducted a systematic analysis of coding experiments published in seven renowned venues in software engineering from 2003 to 2016. In the next step, we systematically evaluated the current solutions to support experiments in SE according to previous findings. Based on the results from previous steps, we proposed our solution. Finally, in the final step, we carried out a case study on replicating coding experiments with the proposed solution. The first study revealed many issues in coding experiments that can be addressed to aid its execution. The second study brought to light which aspects of coding experiments are covered by current solutions. In fact, the majority of general characteristics of coding experiments are adequately addressed by current solutions. However, some context-specific characteristics are not satisfactorily undertaken. Based on found lacks, a metamodel was proposed to specify context-specific coding experiments characteristics. This metamodel can be seen as a specialization of current solution focusing only on coding context characterization. Also, a set of tools were developed to (i) specify models according to the proposed metamodel and (ii) support the experiment execution according to its specification. The last study assessed the proposed solution usage to help researchers carrying out coding activities in experiments. From experiment planner’s perspective, the effort to conduct and collect data was reduced, even considering the extra effort to specify the coding experiment. From participants’ perspective, the proposed solution seemed reasonable to support experiments. However, some issues were identified. Finally, although positive results, performing more assessments including different settings is required to generalize these results. This research focuses only on supporting coding experiments, more precisely planning and execution phases. This work does not deal with other aspects such as data analysis, and we believe current solutions can deal with them. By specifying coding context characteristics, many activities when carrying out a coding experiment can be (semi-)automated, thus contributing to reduce effort to experiment. Moreover, the proposed solution proved adequate for supporting coding experiments, and it is available to support researches around the world through our repository. Experimentos desempenham um papel essencial na avaliação de soluções em engenharia de software (ES). Um campo da ES onde experimentos são frequentemente utilizados é o de desenvolvimento de software. Nele, soluções são propostas para facilitar ou melhorar as atividades de codificação, como diferentes linguagens de programação, desenvolvimento de técnicas, ferramentas e outras soluções. Neste contexto, esta pesquisa tem dois objetivos principais. O primeiro é investigar os experimentos que realizam atividades de codificação (Experimentos de Codificação). Esta investigação deve levantar as características mais comuns de tais experimentos, bem como as soluções atuais que auxiliam a sua realização. O segundo objetivo é propor uma solução para auxiliar estes experimentos de acordo com suas características particulares. Foram adotadas várias metodologias para cada fase da pesquisa. No primeiro estudo, adotamos uma análise sistemática de experimentos de codificação publicados em sete renomados fóruns científicos na engenharia de software entre 2003 a 2016. Depois, realizamos uma avaliação sistemática das atuais soluções para apoiar experimentos em SE. Baseado nos resultados da fases anteriores, propusemos nossa solução. E por fim, conduzimos um quasi-experimento com a solução proposta. Nesse experimento, replicamos um experimento externo que envolvia codificação. O primeiro estudo revelou algumas limitações na realização de experimentos de codificação. Tais limitações podem ser exploradas com o intuito de auxiliar a execução destes experimentos. O segundo estudo identificou lacunas nas atuais soluções que dão apoio a realização de experimentos na engenharia de software. Com base nos resultados anteriores, foi proposto um metamodelo para explicitar as características dos experimentos de codificação que sejam específicas para os seus contextos particulares. Este metamodelo pode ser visto como uma especialização das soluções atuais, focado apenas na caracterização do contexto de codificação. Além disso, um conjunto de ferramentas foi desenvolvido para (i) especificar modelos de acordo com o metamodelo proposto e (ii) apoiar a execução dos experimentos de acordo com a especificação. O último estudo avaliou o uso da solução proposta para auxiliar pesquisadores a realizar experimentos de codificação. Do ponto de vista de um pesquisador, o esforço para conduzir e coletar dados foi reduzido, mesmo considerando o esforço extra para modelar o experimento de codificação. Do ponto de vista dos participantes, a solução proposta aparenta ser útil para apoiar a realização do experimento. No entanto, alguns problemas foram identificados. Finalmente, apesar de resultados positivos, ainda se faz necessário à realização de mais avaliações, incluindo configurações diferentes, para podermos generalizar os resultados obtidos. Esta pesquisa concentra-se apenas no apoio a realização de experimentos de codificação, e mais precisamente as fases de planejamento e execução. Este trabalho não trata de outros aspectos, como análise de dados, e acreditamos que soluções atuais são satisfatórias para lidar com esses outros aspectos. Ao especificar características de contexto de experimento de codificação, muitas atividades do experimento podem ser (semi-) automatizadas, contribuindo assim para reduzir o esforço de experimentação. Além disso, a solução proposta mostrou-se adequada para auxiliar experimentos de codificação, e está disponível para apoiar pesquisas pelo mundo através do nosso repositório.