解决汽车系统开发中的模型复杂性:为分配需求选择系统模型元素

Grischa Liebel, Andreea Olaru, Henrik Lönn, Henrik Kaijser, S. Rajendran, Urban Ingelsson, Richard Berntsson-Svensson
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引用次数: 2

摘要

现代汽车嵌入式系统是由原始设备制造商(OEM)与多个供应商共同开发的。供应商面临的一个关键问题是将OEM的需求规范分配到他们自己的子系统设计中。这是一项困难的手动任务,特别是在复杂的系统上,它需要有关系统设计的专业知识。为了解决这一问题,本文提出了一种开发和评估需求分配辅助工具(RAA)的设计科学研究。该工具提供了搜索和过滤需求和系统模型的功能,即使在存在复杂性的情况下也能实现有效的需求分配。RAA建立在EATOP/Eclipse框架之上,使用EAST-ADL作为系统建模语言。在瑞典汽车行业活跃的17名工程师的定性可用性研究中,对该工具进行了评估和验证。主要发现是,搜索用于了解系统,而过滤用于缩小系统设计的候选元素集。工程师要求进一步的支持,以缩小候选元素的范围,并检查分配是否正确。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Addressing model complexity in automotive system development: Selection of system model elements for allocation of requirements
Modern automotive embedded systems are developed by Original Equipment Manufacturers (OEM) together with multiple suppliers. A key problem for a supplier is to allocate an OEM's requirements specification to their own subsystem design. This is a difficult manual task especially on complex systems and it requires expert knowledge about the system design. To address this problem, this paper presents a design science research to develop and evaluate a Requirements Allocation Assistant tool (RAA). The tool provides functionality to search through and filter requirements and system models to enable efficient requirements allocation even in the presence of complexity. RAA is built on top of the EATOP/Eclipse framework using EAST-ADL as system modelling language. The tool was evaluated and validated during a qualitative usability study with 17 engineers active in the Swedish automotive industry. Key findings are that searching is used to learn about a system, whereas filtering is used to narrow down a set of candidate elements of the system design. Engineers request further support in narrowing down a set of candidate elements and in checking that an allocation is correct.
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