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Exploring Automotive Stakeholder Requirements for Architecture Optimization Support

机译:探索汽车利益相关者对体系结构优化支持的要求

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In automotive system design, system architects increasingly use model-based approaches for defining product line architectures (PLAs). This trend is strongly supported by the emergence of domain standards like EAST-ADL and AUTOSAR that facilitate a system design approach based on systematic reuse. In our work, we focus on the automated exploration of PLA design spaces by means of multi-objective optimization and ask what are challenges that stakeholders face as part of this design task. Optimizing PLA design spaces shares certain challenges with conventional architecture optimization, like conflicting quality goals and potentially vast design spaces. However, PLA optimization exacerbates these challenges by an additional layer of complexity in the system architecture, due to product line variability in the system. Our main focus in this paper is on achieving a better understanding of the stakeholder requirements on a support infrastructure for automated PLA design space exploration based on PLA system models. We also address the challenges that stakeholders must overcome when integrating such an approach into an existing process landscape. For that purpose, we conducted a study with automotive industry experts and analyzed the findings using the qualitative data analysis methods of Grounded Theory. Based on the study results, we derive a set of challenges for the realization and integration of automated and semi-automated PLA design space exploration in industrial environments.
机译:在汽车系统设计中,系统架构师越来越多地使用基于模型的方法来定义产品线架构(PLA)。诸如EAST-ADL和AUTOSAR之类的领域标准的出现为这种趋势提供了有力支持,这些标准促进了基于系统重用的系统设计方法。在我们的工作中,我们专注于通过多目标优化来自动探索PLA设计空间,并询问利益相关者在此设计任务中面临哪些挑战。优化PLA设计空间与常规架构优化面临一些挑战,例如质量目标冲突和潜在的巨大设计空间。但是,由于系统中产品线的可变性,PLA优化会在系统架构中增加一层额外的复杂性,从而加剧了这些挑战。本文的主要重点是更好地了解利益相关者对基于PLA系统模型的自动化PLA设计空间探索的支持基础架构的需求。我们还将解决将此类方法集成到现有流程环境中时,利益相关者必须克服的挑战。为此,我们与汽车行业专家进行了一项研究,并使用“扎根理论”的定性数据分析方法对调查结果进行了分析。基于研究结果,我们得出了在工业环境中实现和集成自动和半自动PLA设计空间探索的一系列挑战。

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