Abstract
Serious Games have been used in professional training to increase employee engagement and improve the results of training initiatives. This work intends to investigate the influence of game elements, in adaptable Serious Games, according to the users' interactions, on the increase of engagement in the game itself and, as the main goal, on the learning results and the transfer of the acquired knowledge and practised skills to the daily work activities. Using the Design Science Research - DSR methodology, this study aims to develop a framework for the development and evaluation of Serious Games to improve the user experience, the learning outcomes, the transfer of knowledge to work situations, and the application of the skills practised in the game in real professional scenarios. This paper presents an initial Framework for Adaptive Serious Games derived from a systematic literature review. The next steps in this investigation are pointed out following the DSR methodology.

Abstract
Testing software is very important, but not done well, resulting in problematic and erroneous software applications. The cause radicates from a skills mismatch between what is needed in industry, the learning needs of students, and the way testing is currently being taught at higher and vocational education institutes. The goal of this project is to identify and design seamless teaching materials for testing that are aligned with industry and learning needs. To represent the entire socio-economic environment that will benefit from the results, this project consortium is composed of a diverse set of partners ranging from universities to small enterprises. The project starts with research in sensemaking and cognitive models when doing and learning testing. Moreover, a study will be done to identify the needs of industry for training and knowledge transfer processes for testing. Based on the outcomes of this research and the study, we will design and develop capsules on teaching software testing including the instructional materials that take into account the cognitive models of students and the industry needs. Finally, we will validate these teaching testing capsules developed during the project.

Abstract
Nowadays, more organizations adopt agile methodologies to guarantee short and frequent delivery times. A plethora of novel approaches and concepts regarding requirements engineering in this context are emerging. User stories are usually informally described as general explanations of software features, written from end-users perspective, while acceptance criteria are high-level conditions that enable their verification. This paper focuses on the art of writing user stories and acceptance criteria, but also on their relationships with other related concepts, such as quality requirements. In the pursuance of deriving guidelines and linguistic patterns to facilitate the writing of requirements specifications, a systematic literature review was conducted to provide a cohesive and comprehensive analysis of such concepts. Despite considerable research on the subject, no formalized model and systematic approach to assist this writing. We provide a coherent analysis of these concepts and related linguistic patterns supported by a running example of specifications built on top of ITLingo RSL, a publicly available tool to enforce the rigorous writing of specification artefacts. We consider that adopting and using the guidelines and patterns from the present discussion contribute to writing better and more consistent requirements.

Abstract

Abstract
OVER A DECADE ago, Jeff Offutt noted, The field of mutation analysis has been growing, both in the number of published papers and the number of active researchers.(33) This trend has since continued, as confirmed by a survey of recent literature.(36) Mutation analysis is the use of well-defined rules defined on syntactic descriptions to make systematic changes to the syntax or to objects developed from the syntax.(33) It has been successfully used in research for assessing test efficacy and as a building block for testing and debugging approaches. It systematically generates syntactic variations, called mutants, of an original program based on a set of mutation operators, which are well-defined program transformation rules. The most common use case of mutation analysis is to assess test efficacy. In this use case, mutants represent faulty versions of the original program, and the ratio of detected mutants quantifies a test suite's efficacy. Empirical evidence supports the use of systematically

Abstract
Research data management (RDM) practices are critical for ensuring research success. Data can assume diverse formats and data in image format have been understudied in RDM. To understand image management habits in research, we have conducted semi-structured interviews with researchers from four research domains. Most researchers do not formally manage their images, nor do they develop RDM plans. They assume that image management is not a topic discussed at project meetings. In turn, they tend to perform some individual practices, depending on the context and their own opinion, such as creating captions to describe the images and organizing and storing the images in specific locations. However, they see these habits as necessary and admit that they will start to do so in a formal and collaborative way with the working group. These results provide valuable information on practical aspects of the use and production of images in research.