使用静态连通性矩阵分析比较多个MATLAB/Simulink模型

Alexander Schlie, Sandro Schulze, Ina Schaefer
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引用次数: 6

摘要

基于模型的语言如MATLAB/Simulink对于嵌入式软件系统的开发至关重要。为了适应不断变化的需求,工程师通常会复制和修改现有系统以创建新的变体。这种重用策略通常被称为“克隆并拥有”,它易于应用并且在短期内是有益的,但是从长期来看,它会带来严重的维护和一致性问题,从而导致大量冗余和类似的资产。此外,在向结构化重用(例如软件产品线)的后期过渡时,不可避免地需要在实际迁移之前比较所有现有的变体。然而,目前的工作主要围绕着两个系统的比较,尽管提出了可以处理更多系统的方法,但这些方法不适用于嵌入式软件系统,如MATLAB/Simulink。在本文中,我们弥合了这一差距,并提出了静态连通性矩阵分析(SCMA),这是一种新的比较程序,允许一次评估多个MATLAB/Simulink模型变体。特别是,我们将模型转换为矩阵形式,用于比较所有模型并识别它们之间的所有相似结构,即使在克隆和拥有过程中模型部分完全重新定位。我们允许工程师裁剪结果,并关注任意的变体子集,在迁移之前进行个人推理。我们从汽车领域提供了可行性研究,表明我们的矩阵表示是合适的,我们的技术是快速和精确的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comparing Multiple MATLAB/Simulink Models Using Static Connectivity Matrix Analysis
Model-based languages such as MATLAB/Simulink are crucial for the development of embedded software systems. To adapt to changing requirements, engineers commonly copy and modify existing systems to create new variants. Commonly referred to as clone-and-own, this reuse strategy is easy to apply and beneficial in the short term, but it entails severe maintenance and consistency issues in the long term, leading to a huge amount of redundant and similar assets. Moreover, a later transition towards structured reuse such as with software product lines inevitably requires the comparison of all existing variants prior to the actual migration. However, current work mostly revolves around the comparison of only two systems and despite approaches proposed that can cope with more, such are not applicable to embedded software systems such as MATLAB/Simulink. In this paper, we bridge this gap and propose Static Connectivity Matrix Analysis (SCMA), a novel comparison procedure that allows for the evaluation of multiple MATLAB/Simulink model variants at once. In particular, we transform models into a matrix form which is used to compare all models and to identify all similar structures between them, even with model parts being completely relocated during clone-and-own. We allow engineers to tailor results and to focus on any arbitrary variant subset, enabling individual reasoning prior to migration. We provide a feasibility study from the automotive domain, showing our matrix representation to be suitable and our technique to be fast and precise.
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