A decision-making process for exploring architectural variants in systems engineering

J. Noir, Sébastien Madelénat, G. Gailliard, C. Labreuche, M. Acher, Olivier Barais, Olivier Constant
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引用次数: 6

Abstract

In systems engineering, practitioners shall explore numerous architectural alternatives until choosing the most adequate variant. The decision-making process is most of the time a manual, time-consuming, and error-prone activity. The exploration and justification of architectural solutions is ad-hoc and mainly consists in a series of tries and errors on the modeling assets. In this paper, we report on an industrial case study in which we apply variability modeling techniques to automate the assessment and comparison of several candidate architectures (variants). We first describe how we can use a model-based approach such as the Common Variability Language (CVL) to specify the architectural variability. We show that the selection of an architectural variant is a multi-criteria decision problem in which there are numerous interactions (veto, favor, complementary) between criteria. We present a tooled process for exploring architectural variants integrating both CVL and the MYRIAD method for assessing and comparing variants based on an explicit preference model coming from the elicitation of stakeholders' concerns. This solution allows understanding differences among variants and their satisfactions with respect to criteria. Beyond variant selection automation improvement, this experiment results highlight that the approach improves rationality in the assessment and provides decision arguments when selecting the preferred variants.
探索系统工程中体系结构变体的决策过程
在系统工程中,从业者应该探索大量的体系结构替代方案,直到选择最合适的变体。决策过程在大多数时候是一个人工的、耗时的、容易出错的活动。架构解决方案的探索和论证是特别的,主要由建模资产上的一系列尝试和错误组成。在本文中,我们报告了一个工业案例研究,其中我们应用可变性建模技术来自动评估和比较几个候选体系结构(变体)。我们首先描述如何使用基于模型的方法,例如通用可变性语言(Common Variability Language, CVL)来指定架构可变性。我们表明,体系结构变体的选择是一个多标准决策问题,其中标准之间存在许多相互作用(否决、赞成、互补)。我们提出了一个工具过程,用于探索架构变体,集成CVL和MYRIAD方法,用于评估和比较基于来自涉众关注的引出的明确偏好模型的变体。该解决方案允许理解变体之间的差异以及它们对标准的满意度。除了改进了变体选择的自动化,实验结果还突出表明,该方法提高了评估的合理性,并在选择首选变体时提供了决策参数。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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