确保变更可追溯性和多模型一致性的模型驱动方法

Claudia Szabo, Yufei Chen
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引用次数: 7

摘要

在模型驱动的工程中,构建应用程序的高级模型,以便独立于实现问题和关注点,对功能性和非功能性需求进行推理。这允许减少维护,缩短开发时间,并允许自动模型更新,系统模型执行和影响评估。作为模型驱动工程的一部分,多建模集成了在不同粒度级别上抽象系统各个方面的模型,例如I/O、行为和功能等,并使用各种特定于领域的建模语言。一个重要的挑战是理解这些模型之间的关系,以保持多模型一致性,因为一个模型中的更改会影响多模型中的其他模型。本文提出了一种多建模体系结构,在语法和语义层面捕捉模型关系。我们定义了一个变化影响的分类法,它依赖于一个关系对应元模型来突出显示和跟踪不同建模环境中变化的影响。遵循通信元模型和相关的变更效果,我们的原型实现确保满足多模型一致性,并通知涉众重大变更。我们对潜艇跟踪系统的案例研究检查了多模型一致性,并强调了跨系统建模工具的变化的影响,这些工具捕获了其功能和行为方面。我们的实验表明了我们的方法的可行性,同时突出了重要的挑战。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Model-Driven Approach for Ensuring Change Traceability and Multi-model Consistency
In model driven engineering, high-level models of an application are constructed to enable reasoning about functional and non-functional requirements independently of implementation issues and concerns. This allows for reduced maintenance, shortens development time, and permits automated model updates, system model executions, and impact assessment. Part of model driven engineering, multi-modeling integrates models that abstract various aspects of the system, such as I/O, behavioral, and functional among others, at different levels of granularity and using various domain specific modeling languages. An important challenge is to understand the relationship between these models towards preserving multi-model consistency as changes in one model affect other models in the multi-model. This paper presents a multi-modeling architecture that captures model relationships at syntactic and semantic levels. We define a taxonomy of change effects that relies on a relationship correspondence meta-model to highlight and trace the impact of changes across various modeling environments. Following the correspondence meta-model and associated change effects, our prototype implementation ensures that multi-model consistency is met and notifies stakeholders of significant changes. Our case study of a submarine tracking system checks multi model consistency and highlights the impact of changes across system modeling tools that capture its functional and behavioral aspects among others. Our experiments show the feasibility of our approach while highlighting important challenges.
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