This paper introduces the new robot programming language LightRocks(Light Weight Robot Coding for Skills), a domain specific language (DSL) for robot programming. The language offers three different level of abstraction for robot programming. On lowest level skills are coded by domain experts. On a more abstract level these skills are supposed to be combined by shop floor workers or technicians to define tasks. The language is designed to allow as much flexibility as necessary on the lowest level of abstraction and is kept as simple as possible with the more abstract layers. A Statechart like model is used to describe the different levels of detail. For this we apply the UML/P and the language workbench MontiCore. To this end we are able to generate code while hiding controller specific implementation details. In addition the development in LightRocks is supported by a generic graphical editor implemented as an Eclipse plugin.
Summary
Efficient testing is a crucial prerequisite to engineer reliable automotive software successfully. However, manually deriving test cases from ambiguous textual requirements is costly and error‐prone. Model‐based software engineering captures requirements in structured, comprehensible, and formal models, which enables early consistency checking and verification. Moreover, these models serve as an indispensable basis for automated test case derivation. To facilitate automated test case derivation for automotive software engineering, we conducted a survey with testing experts of the BMW Group and conceived a method to extend the BMW Group's specification method for requirements, design, and test methodology by model‐based test case derivation. Our method is realized for a variant of systems modeling language activity diagrams tailored toward testing automotive software and a model transformation to derive executable test cases. Hereby, we can address many of the surveyed practitioners' challenges and ultimately facilitate quality assurance for automotive software.
Solving grand environmental societal challenges calls for transdisciplinary and participatory methods in social-ecological research. These methods enable co-designing the research, co-producing the results, and co-creating the impacts together with concerned stakeholders. COVID-19 has had serious impacts on the choice of research methods, but reflections on recent experiences of “moving online” are still rare. In this perspective, we focus on the challenge of adjusting different participatory methods to online formats used in five transdisciplinary social-ecological research projects. The key added value of our research is the lessons learned from a comparison of the pros and cons of adjusting a broader set of methods to online formats. We conclude that combining the adjusted online approaches with well-established face-to-face formats into more inclusive hybrid approaches can enrich and diversify the pool of available methods for postpandemic research. Furthermore, a more diverse group of participants can be engaged in the research process.
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